from skimage.metrics import peak_signal_noise_ratio as psnr
import matplotlib.pyplot as plt
import cv2
import os
import numpy as np
import pandas as pd
import kagglehub
from ultralytics import YOLO
/home/mperpinav/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html from .autonotebook import tqdm as notebook_tqdm
# Nos traemos la BD CBIS-DDSM
path_cbis_ddsm = kagglehub.dataset_download("awsaf49/cbis-ddsm-breast-cancer-image-dataset")
print("Path to dataset files:", path_cbis_ddsm)
Warning: Looks like you're using an outdated `kagglehub` version (installed: 0.3.11), please consider upgrading to the latest version (0.3.12). Path to dataset files: /home/mperpinav/.cache/kagglehub/datasets/awsaf49/cbis-ddsm-breast-cancer-image-dataset/versions/1
La carga la he hecho siguiendo el notebook https://www.kaggle.com/code/gijendrap/breast-cancer-detection/notebook
# Cargamos los csv
dicom_data = pd.read_csv(os.path.join(path_cbis_ddsm, 'csv/dicom_info.csv'))
image_dir = os.path.join(path_cbis_ddsm, 'jpeg')
dicom_data.head()
| file_path | image_path | AccessionNumber | BitsAllocated | BitsStored | BodyPartExamined | Columns | ContentDate | ContentTime | ConversionType | ... | SecondaryCaptureDeviceManufacturerModelName | SeriesDescription | SeriesInstanceUID | SeriesNumber | SmallestImagePixelValue | SpecificCharacterSet | StudyDate | StudyID | StudyInstanceUID | StudyTime | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | CBIS-DDSM/dicom/1.3.6.1.4.1.9590.100.1.2.12930... | CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.129308... | NaN | 16 | 16 | BREAST | 351 | 20160426 | 131732.685 | WSD | ... | MATLAB | cropped images | 1.3.6.1.4.1.9590.100.1.2.129308726812851964007... | 1 | 23078 | ISO_IR 100 | 20160720.0 | DDSM | 1.3.6.1.4.1.9590.100.1.2.271867287611061855725... | 214951.0 |
| 1 | CBIS-DDSM/dicom/1.3.6.1.4.1.9590.100.1.2.24838... | CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.248386... | NaN | 16 | 16 | BREAST | 3526 | 20160426 | 143829.101 | WSD | ... | MATLAB | full mammogram images | 1.3.6.1.4.1.9590.100.1.2.248386742010678582309... | 1 | 0 | ISO_IR 100 | 20160720.0 | DDSM | 1.3.6.1.4.1.9590.100.1.2.161516517311681906612... | 193426.0 |
| 2 | CBIS-DDSM/dicom/1.3.6.1.4.1.9590.100.1.2.26721... | CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.267213... | NaN | 16 | 16 | BREAST | 1546 | 20160503 | 111956.298 | WSD | ... | MATLAB | full mammogram images | 1.3.6.1.4.1.9590.100.1.2.267213171011171858918... | 1 | 0 | ISO_IR 100 | 20160807.0 | DDSM | 1.3.6.1.4.1.9590.100.1.2.291043622711253836701... | 161814.0 |
| 3 | CBIS-DDSM/dicom/1.3.6.1.4.1.9590.100.1.2.38118... | CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.381187... | NaN | 16 | 16 | BREAST | 97 | 20160503 | 115347.770 | WSD | ... | MATLAB | cropped images | 1.3.6.1.4.1.9590.100.1.2.381187369611524586537... | 1 | 32298 | ISO_IR 100 | 20170829.0 | DDSM | 1.3.6.1.4.1.9590.100.1.2.335006093711888937440... | 180109.0 |
| 4 | CBIS-DDSM/dicom/1.3.6.1.4.1.9590.100.1.2.38118... | CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.381187... | NaN | 8 | 8 | Left Breast | 3104 | 20160503 | 115347.770 | WSD | ... | MATLAB | NaN | 1.3.6.1.4.1.9590.100.1.2.381187369611524586537... | 1 | 0 | ISO_IR 100 | NaN | DDSM | 1.3.6.1.4.1.9590.100.1.2.335006093711888937440... | NaN |
5 rows × 38 columns
dicom_data.SeriesDescription.value_counts()
SeriesDescription cropped images 3567 ROI mask images 3247 full mammogram images 2857 Name: count, dtype: int64
cropped_images = dicom_data[dicom_data.SeriesDescription=="cropped images"].image_path
cropped_images.head()
0 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.129308... 3 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.381187... 6 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.153339... 7 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.178994... 10 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.411833... Name: image_path, dtype: object
cropped_images = cropped_images.apply(lambda x: x.replace('CBIS-DDSM/jpeg', image_dir))
cropped_images.head()
0 /home/mperpinav/.cache/kagglehub/datasets/awsa... 3 /home/mperpinav/.cache/kagglehub/datasets/awsa... 6 /home/mperpinav/.cache/kagglehub/datasets/awsa... 7 /home/mperpinav/.cache/kagglehub/datasets/awsa... 10 /home/mperpinav/.cache/kagglehub/datasets/awsa... Name: image_path, dtype: object
full_mammogram_images = dicom_data[dicom_data.SeriesDescription == 'full mammogram images'].image_path
full_mammogram_images.head()
1 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.248386... 2 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.267213... 11 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.210396... 12 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.749566... 15 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.987658... Name: image_path, dtype: object
full_mammogram_images = full_mammogram_images.apply(lambda x: x.replace('CBIS-DDSM/jpeg', image_dir))
full_mammogram_images.head()
1 /home/mperpinav/.cache/kagglehub/datasets/awsa... 2 /home/mperpinav/.cache/kagglehub/datasets/awsa... 11 /home/mperpinav/.cache/kagglehub/datasets/awsa... 12 /home/mperpinav/.cache/kagglehub/datasets/awsa... 15 /home/mperpinav/.cache/kagglehub/datasets/awsa... Name: image_path, dtype: object
ROI_mask_images = dicom_data[dicom_data.SeriesDescription == 'ROI mask images'].image_path
ROI_mask_images.head()
5 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.153339... 8 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.178994... 9 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.411833... 14 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.236373... 20 CBIS-DDSM/jpeg/1.3.6.1.4.1.9590.100.1.2.357008... Name: image_path, dtype: object
ROI_mask_images = ROI_mask_images.apply(lambda x: x.replace('CBIS-DDSM/jpeg', image_dir))
ROI_mask_images.head()
5 /home/mperpinav/.cache/kagglehub/datasets/awsa... 8 /home/mperpinav/.cache/kagglehub/datasets/awsa... 9 /home/mperpinav/.cache/kagglehub/datasets/awsa... 14 /home/mperpinav/.cache/kagglehub/datasets/awsa... 20 /home/mperpinav/.cache/kagglehub/datasets/awsa... Name: image_path, dtype: object
# Creamos una función para corregir el path de las imagenes
def replace_path(sample, old_path, new_path):
return sample.replace(old_path,new_path,regex=True)
from PIL import Image
def plot_smaples(sample, row=15, col=15):
plt.figure(figsize=(row, col))
for i, file in enumerate(sample[0:5]):
cropped_images_show = Image.open(file)
gray_img= cropped_images_show.convert("L")
plt.subplot(1,5,i+1)
plt.imshow(gray_img, cmap='gray')
plt.axis('off')
plt.show()
plot_smaples(cropped_images, 15,15)
plot_smaples(full_mammogram_images,15,15)
plot_smaples(ROI_mask_images)
def get_image_file_name(data, new_dict):
"""
/home
/mperpinav
/.cache
/kagglehub
/datasets
/awsaf49
/cbis-ddsm-breast-cancer-image-dataset
/versions
/1
/jpeg
/1.3.6.1.4.1.9590.100.1.2.153339052913121382622526066491844156138/2-270.jpg
return path at index [12] after split depends on split('\')
"""
for dicom in data:
key = dicom.split('/')[11]
new_dict[key] = dicom
print(f"the length of dataset ==> {len(new_dict.keys())}")
cropped_images_dict = dict()
full_mammo_dict = dict()
roi_img_dict = dict()
get_image_file_name(cropped_images, cropped_images_dict)
get_image_file_name(full_mammogram_images, full_mammo_dict)
get_image_file_name(ROI_mask_images, roi_img_dict)
the length of dataset ==> 3567 the length of dataset ==> 2857 the length of dataset ==> 3247
next(iter((cropped_images.items())))
(0, '/home/mperpinav/.cache/kagglehub/datasets/awsaf49/cbis-ddsm-breast-cancer-image-dataset/versions/1/jpeg/1.3.6.1.4.1.9590.100.1.2.129308726812851964007517874181459556304/1-172.jpg')
def fix_image_path(data):
"""Correct dicom paths to correct image paths."""
for indx, image in enumerate(data.values):
# print(f"Image Path: {image[11]}")
img_name = image[11].split('/')[2]
# print(f"Looking for key: {img_name}") # Debugging step
if img_name in full_mammo_dict:
data.iloc[indx, 11] = full_mammo_dict[img_name]
else:
data.iloc[indx, 11] = None
# print(f"KeyError: '{img_name}' not found in full_mammo_dict") # Debugging step
img_name = image[12].split('/')[2]
if img_name in cropped_images_dict:
data.iloc[indx, 12] = cropped_images_dict[img_name]
else:
data.iloc[indx, 11] = None
# print(f"KeyError: '{img_name}' not found in cropped_images_dict") # Debugging step
img_name = image[13].split('/')[2]
if img_name in roi_img_dict:
data.iloc[indx, 13] = roi_img_dict[img_name]
else:
data.iloc[indx, 13] = None
# Cargamos los csv
mass_train = pd.read_csv(os.path.join(path_cbis_ddsm, 'csv/mass_case_description_train_set.csv'))
mass_test = pd.read_csv(os.path.join(path_cbis_ddsm, 'csv/mass_case_description_test_set.csv'))
mass_train.head()
| patient_id | breast_density | left or right breast | image view | abnormality id | abnormality type | mass shape | mass margins | assessment | pathology | subtlety | image file path | cropped image file path | ROI mask file path | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | P_00001 | 3 | LEFT | CC | 1 | mass | IRREGULAR-ARCHITECTURAL_DISTORTION | SPICULATED | 4 | MALIGNANT | 4 | Mass-Training_P_00001_LEFT_CC/1.3.6.1.4.1.9590... | Mass-Training_P_00001_LEFT_CC_1/1.3.6.1.4.1.95... | Mass-Training_P_00001_LEFT_CC_1/1.3.6.1.4.1.95... |
| 1 | P_00001 | 3 | LEFT | MLO | 1 | mass | IRREGULAR-ARCHITECTURAL_DISTORTION | SPICULATED | 4 | MALIGNANT | 4 | Mass-Training_P_00001_LEFT_MLO/1.3.6.1.4.1.959... | Mass-Training_P_00001_LEFT_MLO_1/1.3.6.1.4.1.9... | Mass-Training_P_00001_LEFT_MLO_1/1.3.6.1.4.1.9... |
| 2 | P_00004 | 3 | LEFT | CC | 1 | mass | ARCHITECTURAL_DISTORTION | ILL_DEFINED | 4 | BENIGN | 3 | Mass-Training_P_00004_LEFT_CC/1.3.6.1.4.1.9590... | Mass-Training_P_00004_LEFT_CC_1/1.3.6.1.4.1.95... | Mass-Training_P_00004_LEFT_CC_1/1.3.6.1.4.1.95... |
| 3 | P_00004 | 3 | LEFT | MLO | 1 | mass | ARCHITECTURAL_DISTORTION | ILL_DEFINED | 4 | BENIGN | 3 | Mass-Training_P_00004_LEFT_MLO/1.3.6.1.4.1.959... | Mass-Training_P_00004_LEFT_MLO_1/1.3.6.1.4.1.9... | Mass-Training_P_00004_LEFT_MLO_1/1.3.6.1.4.1.9... |
| 4 | P_00004 | 3 | RIGHT | MLO | 1 | mass | OVAL | CIRCUMSCRIBED | 4 | BENIGN | 5 | Mass-Training_P_00004_RIGHT_MLO/1.3.6.1.4.1.95... | Mass-Training_P_00004_RIGHT_MLO_1/1.3.6.1.4.1.... | Mass-Training_P_00004_RIGHT_MLO_1/1.3.6.1.4.1.... |
mass_train.iloc[:, 11].head()
0 Mass-Training_P_00001_LEFT_CC/1.3.6.1.4.1.9590... 1 Mass-Training_P_00001_LEFT_MLO/1.3.6.1.4.1.959... 2 Mass-Training_P_00004_LEFT_CC/1.3.6.1.4.1.9590... 3 Mass-Training_P_00004_LEFT_MLO/1.3.6.1.4.1.959... 4 Mass-Training_P_00004_RIGHT_MLO/1.3.6.1.4.1.95... Name: image file path, dtype: object
mass_test.iloc[:, 11].head()
0 Mass-Test_P_00016_LEFT_CC/1.3.6.1.4.1.9590.100... 1 Mass-Test_P_00016_LEFT_MLO/1.3.6.1.4.1.9590.10... 2 Mass-Test_P_00017_LEFT_CC/1.3.6.1.4.1.9590.100... 3 Mass-Test_P_00017_LEFT_MLO/1.3.6.1.4.1.9590.10... 4 Mass-Test_P_00032_RIGHT_CC/1.3.6.1.4.1.9590.10... Name: image file path, dtype: object
fix_image_path(mass_train)
mass_train.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1318 entries, 0 to 1317 Data columns (total 14 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 patient_id 1318 non-null object 1 breast_density 1318 non-null int64 2 left or right breast 1318 non-null object 3 image view 1318 non-null object 4 abnormality id 1318 non-null int64 5 abnormality type 1318 non-null object 6 mass shape 1314 non-null object 7 mass margins 1275 non-null object 8 assessment 1318 non-null int64 9 pathology 1318 non-null object 10 subtlety 1318 non-null int64 11 image file path 1318 non-null object 12 cropped image file path 1318 non-null object 13 ROI mask file path 1318 non-null object dtypes: int64(4), object(10) memory usage: 144.3+ KB
mass_train = mass_train.rename(columns={'left or right breast': 'left_or_right_breast',
'image view': 'image_view',
'abnormality id': 'abnormality_id',
'abnormality type': 'abnormality_type',
'mass shape': 'mass_shape',
'mass margins': 'mass_margins',
'image file path': 'image_file_path',
'cropped image file path': 'cropped_image_file_path',
'ROI mask file path': 'ROI_mask_file_path'})
mass_train.head(5)
| patient_id | breast_density | left_or_right_breast | image_view | abnormality_id | abnormality_type | mass_shape | mass_margins | assessment | pathology | subtlety | image_file_path | cropped_image_file_path | ROI_mask_file_path | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | P_00001 | 3 | LEFT | CC | 1 | mass | IRREGULAR-ARCHITECTURAL_DISTORTION | SPICULATED | 4 | MALIGNANT | 4 | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... |
| 1 | P_00001 | 3 | LEFT | MLO | 1 | mass | IRREGULAR-ARCHITECTURAL_DISTORTION | SPICULATED | 4 | MALIGNANT | 4 | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... |
| 2 | P_00004 | 3 | LEFT | CC | 1 | mass | ARCHITECTURAL_DISTORTION | ILL_DEFINED | 4 | BENIGN | 3 | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... |
| 3 | P_00004 | 3 | LEFT | MLO | 1 | mass | ARCHITECTURAL_DISTORTION | ILL_DEFINED | 4 | BENIGN | 3 | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... |
| 4 | P_00004 | 3 | RIGHT | MLO | 1 | mass | OVAL | CIRCUMSCRIBED | 4 | BENIGN | 5 | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... |
fix_image_path(mass_test)
mass_test = mass_test.rename(columns={'left or right breast': 'left_or_right_breast',
'image view': 'image_view',
'abnormality id': 'abnormality_id',
'abnormality type': 'abnormality_type',
'mass shape': 'mass_shape',
'mass margins': 'mass_margins',
'image file path': 'image_file_path',
'cropped image file path': 'cropped_image_file_path',
'ROI mask file path': 'ROI_mask_file_path'})
mass_test.head(5)
| patient_id | breast_density | left_or_right_breast | image_view | abnormality_id | abnormality_type | mass_shape | mass_margins | assessment | pathology | subtlety | image_file_path | cropped_image_file_path | ROI_mask_file_path | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | P_00016 | 4 | LEFT | CC | 1 | mass | IRREGULAR | SPICULATED | 5 | MALIGNANT | 5 | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... |
| 1 | P_00016 | 4 | LEFT | MLO | 1 | mass | IRREGULAR | SPICULATED | 5 | MALIGNANT | 5 | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... |
| 2 | P_00017 | 2 | LEFT | CC | 1 | mass | ROUND | CIRCUMSCRIBED | 4 | MALIGNANT | 4 | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... |
| 3 | P_00017 | 2 | LEFT | MLO | 1 | mass | ROUND | ILL_DEFINED | 4 | MALIGNANT | 4 | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... |
| 4 | P_00032 | 3 | RIGHT | CC | 1 | mass | ROUND | OBSCURED | 0 | BENIGN | 2 | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... | /home/mperpinav/.cache/kagglehub/datasets/awsa... |
print(f'Shape of mass_train: {mass_train.shape}')
print(f'Shape of mass_test: {mass_test.shape}')
Shape of mass_train: (1318, 14) Shape of mass_test: (378, 14)
def display_images(dataset, column, number):
"""Displays images in dataset, handling missing files and converting formats."""
# create figure and axes
fig, axes = plt.subplots(1, number, figsize=(15, 5))
# Loop through rows and display images
for index, (i, row) in enumerate(dataset.head(number).iterrows()):
image_path = row[column]
# Check if image_path is valid (not None) and exists
if image_path is None or not os.path.exists(image_path):
# print(f"File not found or invalid path: {image_path}")
continue
image = cv2.imread(image_path)
# Handle case when image can't be read
if image is None:
# print(f"Error reading image: {image_path}")
continue
# Convert BGR to RGB if needed (for correct color display)
if len(image.shape) == 3 and image.shape[2] == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
ax = axes[index]
ax.imshow(image, cmap='gray' if len(image.shape) == 2 else None)
ax.set_title(f"{row['pathology']}")
ax.axis('off')
print(np.array(image).shape)
plt.tight_layout()
plt.show()
# Se muestran algunas imagenes de ejemplo
print('Full Mammograms:\n')
display_images(mass_train, 'image_file_path', 5)
print('Cropped Mammograms:\n')
display_images(mass_train, 'cropped_image_file_path', 5)
print('ROI_mask:\n')
display_images(mass_train, 'ROI_mask_file_path', 5)
Full Mammograms: (4808, 3024, 3) (4800, 2656, 3) (5491, 2986, 3) (5491, 3046, 3) (5491, 2911, 3)
Cropped Mammograms: (515, 431, 3) (292, 256, 3) (466, 423, 3) (427, 422, 3) (399, 465, 3)
ROI_mask: (4808, 3024, 3) (4800, 2656, 3) (5491, 2986, 3) (5491, 3046, 3) (5491, 2911, 3)
# Ahora del conjunto test
print('Full Mammograms:\n')
display_images(mass_test, 'image_file_path', 5)
print('Cropped Mammograms:\n')
display_images(mass_test, 'cropped_image_file_path', 5)
print('ROI_mask:\n')
display_images(mass_test, 'ROI_mask_file_path', 5)
Full Mammograms: (4006, 1846, 3) (5491, 2011, 3) (5904, 3200, 3) (5952, 3352, 3) (5116, 2641, 3)
Cropped Mammograms: (384, 385, 3) (369, 328, 3) (214, 195, 3) (220, 225, 3) (405, 376, 3)
ROI_mask: (4006, 1846, 3) (5491, 2011, 3) (5904, 3200, 3) (5952, 3352, 3) (5116, 2641, 3)
# Para no hacer el preprocesamiento dos veces, unimos mass_train y mass_test
mass_data = pd.concat([mass_train, mass_test], axis=0)
# Mostramos como ha quedado la distribución
fig,ax = plt.subplots()
fig.suptitle("Distribución clases conjunto mass")
num_mass = mass_data['pathology'].value_counts()
ax.bar( num_mass.index, num_mass, color = "#027a6d")
plt.show()
print("\nConteo mass:")
print(num_mass)
Conteo mass: pathology MALIGNANT 784 BENIGN 771 BENIGN_WITHOUT_CALLBACK 141 Name: count, dtype: int64
# Se juntan las clases BENIGN y BENIGN_WITHOUT_CALLBACK para tener una distribución binaria.
# Unificacion de las clases BENIGN y BENIGN_WITHOUT_CALLBACK como BENIGN
mass_data['pathology'] = mass_data['pathology'].replace('BENIGN_WITHOUT_CALLBACK', 'BENIGN')
print("\nConteo mass:")
num_mass = mass_data['pathology'].value_counts()
print(num_mass)
Conteo mass: pathology BENIGN 912 MALIGNANT 784 Name: count, dtype: int64
# Mostramos como ha quedado la distribución
fig,ax = plt.subplots()
fig.suptitle("Distribución clases conjunto mass")
ax.bar( num_mass.index, num_mass, color = "#027a6d")
plt.show()
Se obseva un sesgo de clases, lo tendremos que tratar durante el entrenamiento del modelo.
# Guardamos los tipos de anomalia que tenemos
anomalia_tipes = []
for i in mass_data['pathology'].unique():
anomalia_tipes.append(i)
print(anomalia_tipes)
['MALIGNANT', 'BENIGN']
Empezamos comprobando que todas las imágenes están en escala de grises
mass_data.columns
Index(['patient_id', 'breast_density', 'left_or_right_breast', 'image_view',
'abnormality_id', 'abnormality_type', 'mass_shape', 'mass_margins',
'assessment', 'pathology', 'subtlety', 'image_file_path',
'cropped_image_file_path', 'ROI_mask_file_path'],
dtype='object')
count = 0
for i in range(len(mass_data)):
img = Image.open(mass_data['image_file_path'].iloc[i])
# Verificamos el modo de color
if img.mode != 'L': # 'L' es para escala de grises
count += 1
print("Tenemos {} imágenes que no están en escala de grises".format(count))
Tenemos 0 imágenes que no están en escala de grises
https://www.datacamp.com/es/blog/yolo-object-detection-explained Cambiamos el tamaño de las imagenes a 640X640
import shutil
# Redimensionamos el tamaño de las imagenes del conjunto de entrenamiento
img_height = 640
img_width = 640
shutil.rmtree('Resized_mass_data', ignore_errors=True)
os.makedirs('Resized_mass_data')
resize_mass_data = []
for i, path in enumerate(mass_data['image_file_path']):
# Cargamos la imagen en escala de grises
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
resized_img = cv2.resize(img,(img_height,img_width))
# Guardamos las imagenes redimensionadas
output_path = f'Resized_mass_data/resized_{i}.jpg'
cv2.imwrite(output_path, resized_img)
resize_mass_data.append(output_path)
# Guardamos en el dataset para tener el etiquetado
mass_data['resize_path'] = resize_mass_data
Para este apartado he consultado: https://docs.opencv.org/4.x/d5/d69/tutorial_py_non_local_means.html https://docs.opencv.org/4.x/d1/d79/group__photo__denoise.html#ga4c6b0031f56ea3f98f768881279ffe93
# Lo aplicamos a todo el conjunto de entrenamiento
psnr_values = []
# Guardamos las imagenes filtradas en una nueva ruta
mass_red_ruido = []
shutil.rmtree('Red_Ruido_mass', ignore_errors=True)
os.makedirs('Red_Ruido_mass')
for i, path in enumerate(mass_data['resize_path']):
# Cargamos la imagen en escala de grises
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
# Aplicamos reducción de ruido
img_filtered = cv2.fastNlMeansDenoising(img,3,3,7,21) # Dejamos los valores recomendados por CV
# Calcular el PSNR entre la imagen original y la imagen filtrada
current_psnr = psnr(img, img_filtered)
# Guardar el PSNR para esta imagen
psnr_values.append(current_psnr)
# Guardamos las imagenes preprocesadas
output_path = f'Red_Ruido_mass/red_ruido_{i}.jpg'
cv2.imwrite(output_path, img_filtered)
mass_red_ruido.append(output_path)
mass_data['red_ruido_path'] = mass_red_ruido
print("El PSNR medio para el conjunto mass es: {}".format(np.mean(psnr_values)))
El PSNR medio para el conjunto mass es: 44.574180066128434
print('Reducción de ruido:\n')
display_images(mass_data, 'red_ruido_path', 5)
Reducción de ruido: (640, 640, 3) (640, 640, 3) (640, 640, 3) (640, 640, 3) (640, 640, 3)
shutil.rmtree('CLAHE_mass', ignore_errors=True)
os.makedirs('CLAHE_mass')
# Guardamos las nuevas rutas
clahe_mass = []
# Recorremos todo el conjunto de entrenamiento
for i, path in enumerate(mass_data['red_ruido_path']):
# Cargamos la imagen en escala de grises
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
# Aplicamos el preprocesamiento con CLAHE
clahe = cv2.createCLAHE()
cl1 = clahe.apply(img)
# Guardamos las imagenes preprocesadas
output_path = f'CLAHE_mass/clahe_{i}.jpg'
cv2.imwrite(output_path, cl1)
clahe_mass.append(output_path)
# Guardamos en el dataset para tener el etiquetado
mass_data['clahe_path'] = clahe_mass
print('Mejora de contraste con CLAHE:\n')
display_images(mass_data, 'clahe_path', 5)
print('Imagen con reducción de ruido:\n')
display_images(mass_data, 'red_ruido_path',5)
Mejora de contraste con CLAHE: (640, 640, 3) (640, 640, 3) (640, 640, 3) (640, 640, 3) (640, 640, 3)
Imagen con reducción de ruido: (640, 640, 3) (640, 640, 3) (640, 640, 3) (640, 640, 3) (640, 640, 3)
Vamos a separar el conjunto de datos en un conjunto de entrenamiento y otro de prueba. La distribución quedará como el 80\% para el conjunto de entrenamiento y 20\% para el conjunto test
from sklearn.model_selection import train_test_split
# Hacemos los conjuntos de train y test
X_mass = mass_data.drop(columns=['pathology'])
y_mass = mass_data['pathology']
X_train_mass, X_val_mass, y_train_mass, y_val_mass = train_test_split(
X_mass, y_mass,
test_size=0.2,
random_state=42,
stratify=y_mass
)
# Nos guardamos los datasets
mass_train = pd.concat([X_train_mass, y_train_mass], axis=1)
mass_val = pd.concat([X_val_mass, y_val_mass], axis=1)
# Comprobamos que la distribución entre conjunto train y test es correcta
fig, ax = plt.subplots()
fig.suptitle("Distibución clases conjunto mass train vs val")
num_mass_train = mass_train['pathology'].value_counts()
num_mass_val = mass_val['pathology'].value_counts()
ax.bar(num_mass_train.index,num_mass_train, color = "#027a6d", label = 'train' )
ax.bar(num_mass_val.index, num_mass_val, color = "#44c27f", label = 'val')
plt.legend()
plt.show()
print("Distribución del conjunto train mass:")
for clase, num in (100 * mass_train['pathology'].value_counts() / (mass_train['pathology'].value_counts() + mass_val['pathology'].value_counts())).items():
print(f"{clase}: {num:.2f}%")
print("Distribución del conjunto val mass:")
for clase, num in (100 * mass_val['pathology'].value_counts() / (mass_train['pathology'].value_counts() + mass_val['pathology'].value_counts())).items():
print(f"{clase}: {num:.2f}%")
Distribución del conjunto train mass: BENIGN: 79.93% MALIGNANT: 79.97% Distribución del conjunto val mass: BENIGN: 20.07% MALIGNANT: 20.03%
Primero organizamos las imagenes en carpetas de entrenamiento y test como espaera YOLO, además de otra carpeta con las etiquetas de las imagenes de entrenamiento y test. https://docs.ultralytics.com/es/datasets/detect/#ultralytics-yolo-format
# yolo espera que las etiquetas de clase sean numericas
label_map = {'BENIGN': 0, 'MALIGNANT': 1}
mass_data['pathology'] = mass_data['pathology'].map(label_map)
mass_train['pathology'] = mass_train['pathology'].map(label_map)
mass_val['pathology'] = mass_val['pathology'].map(label_map)
# Creamos la estructura
base_dir = 'final_dataset_yolo'
for split in ['train', 'val']:
os.makedirs(os.path.join(base_dir, 'images', split), exist_ok=True)
os.makedirs(os.path.join(base_dir, 'labels', split), exist_ok=True)
Se extraen los bounding boxes que necesita YOLO https://stackoverflow.com/questions/73282135/computing-bounding-boxes-from-a-mask-image-tensorflow-or-other
from tqdm import tqdm
from shutil import copyfile
import pydicom
from skimage.measure import label, regionprops, find_contours
def mask_to_bbox(mask, min_area=50):
bboxes = []
# Asegurar que la máscara es binaria
_, mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)
# Etiquetar regiones conectadas
lbl = label(mask)
props = regionprops(lbl)
for prop in props:
if prop.area < min_area:
continue
x1 = prop.bbox[1]
y1 = prop.bbox[0]
x2 = prop.bbox[3]
y2 = prop.bbox[2]
bboxes.append([x1, y1, x2, y2])
return bboxes
def create_yolo_labels(bboxes, img_w, img_h, label_path, class_id):
with open(label_path, 'w') as f:
for bbox in bboxes:
x_min, y_min, x_max, y_max = bbox
x_center = ((x_min + x_max) / 2) / img_w
y_center = ((y_min + y_max) / 2) / img_h
width = (x_max - x_min) / img_w
height = (y_max - y_min) / img_h
f.write(f"{class_id} {x_center} {y_center} {width} {height}\n")
# Se crea el .txt para YOLO de todas las imagenes
def process_split(df, split, base_dir):
for idx, row in tqdm(df.iterrows(), total=len(df), desc=f"Processing {split}"):
# Conseguimos la ruta de las imagenes y sus mascaras
mask_path = row['ROI_mask_file_path']
img_path = row['clahe_path']
class_id = row['pathology']
img_dest = os.path.join(base_dir, 'images', split, f"{idx}.jpg")
os.makedirs(os.path.dirname(img_dest), exist_ok=True)
copyfile(img_path, img_dest)
img = Image.open(img_path)
img_w, img_h = img.size
# Leer máscara y obtener bboxes
mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
mask = cv2.resize(mask, (img_w, img_h), interpolation=cv2.INTER_NEAREST)
bboxes = mask_to_bbox(mask)
# Crear .txt con anotaciones YOLO
label_dest = os.path.join(base_dir, 'labels', split, f"{idx}.txt")
os.makedirs(os.path.dirname(label_dest), exist_ok=True)
create_yolo_labels(bboxes, img_w, img_h, label_dest, class_id)
process_split(mass_train, 'train', base_dir)
Processing train: 100%|██████████| 1356/1356 [00:55<00:00, 24.64it/s]
process_split(mass_val, 'val', base_dir)
Processing val: 100%|██████████| 340/340 [00:13<00:00, 24.68it/s]
# Creamos el archivo yaml
content = f"""train: {os.path.abspath(base_dir)}/images/train
val: {os.path.abspath(base_dir)}/images/val
nc: 2
names: ['BENIGN', 'MALIGNANT']
"""
with open(os.path.join(base_dir, 'data.yaml'), 'w') as f:
f.write(content)
image_path = '/home/mperpinav/final_dataset_yolo/images/val/300.jpg'
label_path = '/home/mperpinav/final_dataset_yolo/labels/val/300.txt'
import cv2
import os
import matplotlib.pyplot as plt
def draw_yolo_boxes(image_path, label_path):
img = cv2.imread(image_path)
if img is None:
raise ValueError(f"Imagen no encontrada o no compatible: {image_path}")
h, w = img.shape[:2]
print(f"Imagen cargada: {image_path} - tamaño: {w}x{h}")
if not os.path.exists(label_path):
print("No existe archivo de labels:", label_path)
return img
with open(label_path, 'r') as f:
for line in f:
parts = line.strip().split()
if len(parts) != 5:
print("Línea inválida:", line)
continue
class_id, x_center, y_center, box_w, box_h = map(float, parts)
# Convertir a coordenadas de píxeles
x_center *= w
y_center *= h
box_w *= w
box_h *= h
x1 = int(x_center - box_w / 2)
y1 = int(y_center - box_h / 2)
x2 = int(x_center + box_w / 2)
y2 = int(y_center + box_h / 2)
print(f"Dibujando caja clase {int(class_id)}: ({x1}, {y1}) - ({x2}, {y2})")
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
return img
def show_image(img):
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(10, 10))
plt.imshow(img_rgb)
plt.axis('off')
plt.show()
img_with_boxes = draw_yolo_boxes(image_path, label_path)
show_image(img_with_boxes)
Imagen cargada: /home/mperpinav/final_dataset_yolo/images/val/300.jpg - tamaño: 640x640 Dibujando caja clase 0: (411, 339) - (461, 365)
print(base_dir)
final_dataset_yolo
# Entrenamiento modelo mass YOLO v8
model = YOLO('yolov8n.pt')
data_mass_path = os.path.join(base_dir, 'data.yaml')
model.train(data=data_mass_path, epochs=100, patience = 10)
New https://pypi.org/project/ultralytics/8.3.125 available 😃 Update with 'pip install -U ultralytics' Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB) engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=final_dataset_yolo/data.yaml, epochs=100, time=None, patience=10, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train40, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, cutmix=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train40 Overriding model.yaml nc=80 with nc=2 from n params module arguments 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] 22 [15, 18, 21] 1 751702 ultralytics.nn.modules.head.Detect [2, [64, 128, 256]] Model summary: 129 layers, 3,011,238 parameters, 3,011,222 gradients, 8.2 GFLOPs Transferred 319/355 items from pretrained weights Freezing layer 'model.22.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ✅ train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 951.3±332.0 MB/s, size: 82.0 KB)
train: Scanning /home/mperpinav/final_dataset_yolo/labels/train... 1124 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1124/1124 [00:01<00:00, 663.22it/s]
train: New cache created: /home/mperpinav/final_dataset_yolo/labels/train.cache
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 579.8±427.2 MB/s, size: 73.0 KB)
val: Scanning /home/mperpinav/final_dataset_yolo/labels/val... 324 images, 0 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:00<00:00, 687.47it/s]
val: New cache created: /home/mperpinav/final_dataset_yolo/labels/val.cache Plotting labels to runs/detect/train40/labels.jpg... optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0) Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to runs/detect/train40 Starting training for 100 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/100 2.19G 2.348 4.978 1.714 6 640: 100%|██████████| 71/71 [00:16<00:00, 4.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.50it/s]
all 324 324 0.00115 0.346 0.0322 0.0119
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/100 2.47G 2.183 3.88 1.626 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.92it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.85it/s]
all 324 324 0.15 0.118 0.0619 0.023
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/100 2.49G 2.203 3.419 1.614 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.84it/s]
all 324 324 0.0884 0.141 0.0609 0.0221
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/100 2.5G 2.123 3.086 1.59 8 640: 100%|██████████| 71/71 [00:14<00:00, 5.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.88it/s]
all 324 324 0.19 0.204 0.106 0.0389
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/100 2.51G 2.182 2.999 1.599 7 640: 100%|██████████| 71/71 [00:14<00:00, 5.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.73it/s]
all 324 324 0.247 0.171 0.141 0.0578
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/100 2.54G 2.108 2.81 1.598 5 640: 100%|██████████| 71/71 [00:14<00:00, 5.05it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.00it/s]
all 324 324 0.201 0.372 0.175 0.0658
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/100 2.55G 2.14 2.703 1.607 3 640: 100%|██████████| 71/71 [00:14<00:00, 5.04it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.13it/s]
all 324 324 0.311 0.27 0.191 0.0734
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/100 2.57G 2.074 2.672 1.616 6 640: 100%|██████████| 71/71 [00:14<00:00, 5.04it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.03it/s]
all 324 324 0.277 0.251 0.19 0.0739
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/100 2.58G 2.045 2.624 1.536 7 640: 100%|██████████| 71/71 [00:14<00:00, 5.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.02it/s]
all 324 324 0.289 0.311 0.204 0.0779
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/100 2.6G 2.091 2.653 1.584 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.99it/s]
all 324 324 0.293 0.344 0.236 0.0989
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/100 2.62G 2.047 2.537 1.546 3 640: 100%|██████████| 71/71 [00:14<00:00, 5.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.99it/s]
all 324 324 0.284 0.333 0.198 0.0776
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/100 2.64G 2.026 2.566 1.532 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.95it/s]
all 324 324 0.344 0.346 0.248 0.102
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/100 2.65G 2.06 2.593 1.573 4 640: 100%|██████████| 71/71 [00:14<00:00, 5.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.95it/s]
all 324 324 0.276 0.365 0.221 0.0821
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/100 2.67G 2.06 2.518 1.582 5 640: 100%|██████████| 71/71 [00:14<00:00, 5.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.02it/s]
all 324 324 0.333 0.326 0.232 0.0872
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/100 2.69G 2.029 2.495 1.56 4 640: 100%|██████████| 71/71 [00:14<00:00, 5.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.08it/s]
all 324 324 0.31 0.352 0.235 0.0941
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/100 2.71G 1.996 2.415 1.534 5 640: 100%|██████████| 71/71 [00:14<00:00, 5.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.93it/s]
all 324 324 0.375 0.344 0.279 0.109
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/100 2.71G 2.023 2.447 1.54 6 640: 100%|██████████| 71/71 [00:14<00:00, 5.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.01it/s]
all 324 324 0.315 0.33 0.221 0.0895
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/100 2.74G 2.003 2.454 1.567 9 640: 100%|██████████| 71/71 [00:14<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.99it/s]
all 324 324 0.351 0.377 0.282 0.115
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/100 2.76G 1.971 2.409 1.53 8 640: 100%|██████████| 71/71 [00:14<00:00, 5.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.11it/s]
all 324 324 0.312 0.343 0.247 0.0971
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/100 2.77G 1.986 2.39 1.524 4 640: 100%|██████████| 71/71 [00:14<00:00, 5.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.97it/s]
all 324 324 0.272 0.351 0.242 0.0931
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/100 2.78G 2.017 2.44 1.586 5 640: 100%|██████████| 71/71 [00:14<00:00, 5.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.06it/s]
all 324 324 0.346 0.381 0.268 0.11
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/100 2.81G 1.961 2.281 1.523 4 640: 100%|██████████| 71/71 [00:14<00:00, 5.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.94it/s]
all 324 324 0.332 0.394 0.261 0.0998
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/100 2.82G 1.974 2.384 1.543 5 640: 100%|██████████| 71/71 [00:14<00:00, 5.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.98it/s]
all 324 324 0.353 0.354 0.283 0.105
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/100 2.84G 1.956 2.424 1.573 5 640: 100%|██████████| 71/71 [00:14<00:00, 5.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.10it/s]
all 324 324 0.372 0.386 0.279 0.116
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/100 2.85G 1.919 2.333 1.51 9 640: 100%|██████████| 71/71 [00:14<00:00, 5.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.09it/s]
all 324 324 0.371 0.361 0.293 0.118
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/100 2.88G 1.895 2.275 1.486 10 640: 100%|██████████| 71/71 [00:14<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.88it/s]
all 324 324 0.371 0.359 0.302 0.121
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/100 2.89G 1.924 2.32 1.508 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.99it/s]
all 324 324 0.379 0.426 0.303 0.12
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/100 2.91G 1.93 2.288 1.525 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.04it/s]
all 324 324 0.398 0.403 0.328 0.129
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/100 2.92G 1.907 2.303 1.508 9 640: 100%|██████████| 71/71 [00:14<00:00, 5.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.06it/s]
all 324 324 0.374 0.382 0.301 0.12
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/100 2.95G 1.921 2.298 1.488 10 640: 100%|██████████| 71/71 [00:14<00:00, 5.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.05it/s]
all 324 324 0.403 0.412 0.303 0.117
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/100 2.96G 1.913 2.204 1.511 7 640: 100%|██████████| 71/71 [00:14<00:00, 5.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.82it/s]
all 324 324 0.369 0.394 0.298 0.124
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/100 2.98G 1.893 2.241 1.47 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.99it/s]
all 324 324 0.331 0.386 0.272 0.111
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/100 2.99G 1.892 2.138 1.487 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.95it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.05it/s]
all 324 324 0.387 0.359 0.317 0.128
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/100 3.01G 1.9 2.248 1.49 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.93it/s]
all 324 324 0.401 0.387 0.315 0.126
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/100 3.03G 1.892 2.149 1.505 4 640: 100%|██████████| 71/71 [00:14<00:00, 5.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.00it/s]
all 324 324 0.36 0.345 0.296 0.116
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/100 3.05G 1.901 2.149 1.494 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.94it/s]
all 324 324 0.359 0.354 0.285 0.12
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/100 3.05G 1.848 2.18 1.464 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.95it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.01it/s]
all 324 324 0.395 0.345 0.299 0.123
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/100 3.08G 1.925 2.189 1.515 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.79it/s]
all 324 324 0.434 0.374 0.309 0.123
EarlyStopping: Training stopped early as no improvement observed in last 10 epochs. Best results observed at epoch 28, best model saved as best.pt.
To update EarlyStopping(patience=10) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.
38 epochs completed in 0.185 hours. Optimizer stripped from runs/detect/train40/weights/last.pt, 6.2MB Optimizer stripped from runs/detect/train40/weights/best.pt, 6.2MB Validating runs/detect/train40/weights/best.pt... Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB) Model summary (fused): 72 layers, 3,006,038 parameters, 0 gradients, 8.1 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.31it/s]
all 324 324 0.402 0.401 0.328 0.129
BENIGN 174 174 0.416 0.336 0.319 0.13
MALIGNANT 150 150 0.387 0.467 0.337 0.128
Speed: 0.2ms preprocess, 2.6ms inference, 0.0ms loss, 2.0ms postprocess per image
Results saved to runs/detect/train40
ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([0, 1])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7fc6ca910d30>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
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0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,
0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,
0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,
0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,
0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,
0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,
0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.93678, 0.93678, 0.91379, ..., 0, 0, 0],
[ 0.90667, 0.90667, 0.88, ..., 0, 0, 0]]), 'Confidence', 'Recall']]
fitness: np.float64(0.1490300014987881)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.13011, 0.12827])
names: {0: 'BENIGN', 1: 'MALIGNANT'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.4015510104609523), 'metrics/recall(B)': np.float64(0.40146752355804083), 'metrics/mAP50(B)': np.float64(0.3275996103629044), 'metrics/mAP50-95(B)': np.float64(0.1291889338472196), 'fitness': np.float64(0.1490300014987881)}
save_dir: PosixPath('runs/detect/train40')
speed: {'preprocess': 0.17833083086175683, 'inference': 2.645663356546451, 'loss': 0.006058980584328557, 'postprocess': 1.962173750256131}
task: 'detect'
# Entrenamiento YOLOv8
!ls runs/detect/train40
/etc/bash.bashrc: line 5: export: `:/home/mperpinav/.local/lib/python3.10/site-packages/cv2/../../lib64:/usr/local/cuda-12.2/lib64': not a valid identifier args.yaml PR_curve.png val_batch0_labels.jpg confusion_matrix_normalized.png R_curve.png val_batch0_pred.jpg confusion_matrix.png results.csv val_batch1_labels.jpg F1_curve.png results.png val_batch1_pred.jpg labels_correlogram.jpg train_batch0.jpg val_batch2_labels.jpg labels.jpg train_batch1.jpg val_batch2_pred.jpg P_curve.png train_batch2.jpg weights
# Entrenamiento modelo mass YOLO v11
model_11 = YOLO("yolo11n.pt")
data_mass_path = os.path.join(base_dir, 'data.yaml')
model_11.train(data=data_mass_path, epochs=100, patience = 10)
New https://pypi.org/project/ultralytics/8.3.125 available 😃 Update with 'pip install -U ultralytics' Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB) engine/trainer: task=detect, mode=train, model=yolo11n.pt, data=final_dataset_yolo/data.yaml, epochs=100, time=None, patience=10, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train41, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, cutmix=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train41 Overriding model.yaml nc=80 with nc=2 from n params module arguments 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25] 3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] 5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False] 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False] 17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] 19 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False] 20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] 22 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True] 23 [16, 19, 22] 1 431062 ultralytics.nn.modules.head.Detect [2, [64, 128, 256]] YOLO11n summary: 181 layers, 2,590,230 parameters, 2,590,214 gradients, 6.4 GFLOPs Transferred 448/499 items from pretrained weights Freezing layer 'model.23.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ✅ train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 617.5±215.7 MB/s, size: 82.0 KB)
train: Scanning /home/mperpinav/final_dataset_yolo/labels/train.cache... 1124 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1124/1124 [00:00<?, ?it/s]
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 407.5±257.2 MB/s, size: 73.0 KB)
val: Scanning /home/mperpinav/final_dataset_yolo/labels/val.cache... 324 images, 0 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:00<?, ?it/s]
Plotting labels to runs/detect/train41/labels.jpg... optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0) Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to runs/detect/train41 Starting training for 100 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/100 2.8G 2.357 5.12 1.706 6 640: 100%|██████████| 71/71 [00:22<00:00, 3.14it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.35it/s]
all 324 324 0.486 0.0157 0.0921 0.0268
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/100 3.04G 2.201 3.997 1.608 4 640: 100%|██████████| 71/71 [00:18<00:00, 3.91it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.37it/s]
all 324 324 0.117 0.197 0.0686 0.0216
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/100 3.04G 2.211 3.502 1.61 8 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.71it/s]
all 324 324 0.165 0.247 0.114 0.0385
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/100 3.04G 2.167 3.144 1.613 8 640: 100%|██████████| 71/71 [00:17<00:00, 4.04it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.68it/s]
all 324 324 0.187 0.0954 0.112 0.0446
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/100 3.04G 2.19 2.974 1.645 7 640: 100%|██████████| 71/71 [00:17<00:00, 4.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.67it/s]
all 324 324 0.217 0.287 0.155 0.0557
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/100 3.04G 2.108 2.802 1.622 5 640: 100%|██████████| 71/71 [00:17<00:00, 4.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.69it/s]
all 324 324 0.205 0.3 0.134 0.053
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/100 3.04G 2.125 2.757 1.626 3 640: 100%|██████████| 71/71 [00:17<00:00, 4.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.79it/s]
all 324 324 0.201 0.252 0.133 0.0448
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/100 3.04G 2.101 2.7 1.644 6 640: 100%|██████████| 71/71 [00:17<00:00, 4.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.78it/s]
all 324 324 0.299 0.269 0.201 0.078
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/100 3.04G 2.038 2.637 1.559 7 640: 100%|██████████| 71/71 [00:19<00:00, 3.57it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.82it/s]
all 324 324 0.303 0.348 0.213 0.0785
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/100 3.04G 2.1 2.671 1.605 6 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.44it/s]
all 324 324 0.295 0.265 0.181 0.0757
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/100 3.04G 2.089 2.565 1.603 3 640: 100%|██████████| 71/71 [00:17<00:00, 4.04it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.80it/s]
all 324 324 0.239 0.257 0.156 0.0624
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/100 3.04G 2.048 2.595 1.57 6 640: 100%|██████████| 71/71 [00:18<00:00, 3.94it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.65it/s]
all 324 324 0.348 0.27 0.225 0.0894
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/100 3.04G 2.063 2.598 1.571 4 640: 100%|██████████| 71/71 [00:17<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.63it/s]
all 324 324 0.329 0.357 0.268 0.11
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/100 3.04G 2.067 2.512 1.585 5 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.77it/s]
all 324 324 0.296 0.333 0.221 0.0884
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/100 3.04G 2.054 2.51 1.588 4 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.75it/s]
all 324 324 0.317 0.302 0.243 0.0871
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/100 3.04G 2.013 2.431 1.573 5 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.78it/s]
all 324 324 0.326 0.351 0.258 0.105
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/100 3.04G 2.041 2.441 1.561 6 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.79it/s]
all 324 324 0.259 0.284 0.171 0.0667
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/100 3.04G 1.98 2.437 1.566 9 640: 100%|██████████| 71/71 [00:17<00:00, 4.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.82it/s]
all 324 324 0.389 0.356 0.289 0.118
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/100 3.04G 1.98 2.425 1.547 8 640: 100%|██████████| 71/71 [00:17<00:00, 4.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.51it/s]
all 324 324 0.347 0.329 0.247 0.103
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/100 3.04G 1.989 2.41 1.534 4 640: 100%|██████████| 71/71 [00:17<00:00, 4.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.81it/s]
all 324 324 0.348 0.319 0.234 0.0926
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/100 3.04G 2.007 2.423 1.584 5 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.82it/s]
all 324 324 0.385 0.316 0.242 0.101
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/100 3.04G 1.976 2.304 1.524 4 640: 100%|██████████| 71/71 [00:17<00:00, 4.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.87it/s]
all 324 324 0.386 0.352 0.286 0.113
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/100 3.04G 1.998 2.394 1.556 5 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.77it/s]
all 324 324 0.345 0.338 0.283 0.112
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/100 3.04G 1.996 2.447 1.608 5 640: 100%|██████████| 71/71 [00:17<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.57it/s]
all 324 324 0.34 0.351 0.264 0.109
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/100 3.04G 1.951 2.336 1.528 9 640: 100%|██████████| 71/71 [00:17<00:00, 3.95it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.82it/s]
all 324 324 0.322 0.347 0.252 0.0989
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/100 3.04G 1.922 2.303 1.509 10 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.77it/s]
all 324 324 0.338 0.339 0.281 0.118
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/100 3.04G 1.958 2.361 1.529 8 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.69it/s]
all 324 324 0.456 0.355 0.294 0.124
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/100 3.04G 1.954 2.337 1.539 3 640: 100%|██████████| 71/71 [00:17<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.85it/s]
all 324 324 0.367 0.284 0.266 0.109
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/100 3.04G 1.925 2.329 1.528 9 640: 100%|██████████| 71/71 [00:17<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.77it/s]
all 324 324 0.336 0.342 0.246 0.1
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/100 3.04G 1.93 2.341 1.509 10 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.81it/s]
all 324 324 0.366 0.399 0.314 0.119
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/100 3.04G 1.917 2.28 1.51 7 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.49it/s]
all 324 324 0.409 0.402 0.304 0.122
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/100 3.04G 1.932 2.26 1.484 5 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.83it/s]
all 324 324 0.306 0.375 0.302 0.121
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/100 3.04G 1.921 2.196 1.503 3 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.82it/s]
all 324 324 0.39 0.358 0.309 0.118
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/100 3.04G 1.923 2.295 1.493 6 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.63it/s]
all 324 324 0.374 0.348 0.259 0.106
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/100 3.04G 1.892 2.173 1.501 4 640: 100%|██████████| 71/71 [00:17<00:00, 4.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.63it/s]
all 324 324 0.388 0.361 0.313 0.122
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/100 3.04G 1.932 2.206 1.505 8 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.83it/s]
all 324 324 0.39 0.319 0.283 0.122
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/100 3.04G 1.871 2.222 1.464 8 640: 100%|██████████| 71/71 [00:17<00:00, 3.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.72it/s]
all 324 324 0.424 0.38 0.324 0.129
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/100 3.04G 1.935 2.248 1.516 7 640: 100%|██████████| 71/71 [00:20<00:00, 3.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.74it/s]
all 324 324 0.4 0.396 0.326 0.134
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
39/100 3.04G 1.871 2.192 1.464 7 640: 100%|██████████| 71/71 [00:18<00:00, 3.81it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.52it/s]
all 324 324 0.278 0.323 0.233 0.0973
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
40/100 3.04G 1.925 2.223 1.5 6 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.67it/s]
all 324 324 0.417 0.387 0.305 0.121
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
41/100 3.04G 1.888 2.135 1.502 6 640: 100%|██████████| 71/71 [00:17<00:00, 3.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.83it/s]
all 324 324 0.367 0.363 0.299 0.119
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
42/100 3.04G 1.926 2.17 1.529 5 640: 100%|██████████| 71/71 [00:17<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.51it/s]
all 324 324 0.375 0.347 0.269 0.109
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
43/100 3.04G 1.863 2.193 1.465 9 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.72it/s]
all 324 324 0.333 0.42 0.279 0.11
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
44/100 3.04G 1.876 2.143 1.469 7 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.80it/s]
all 324 324 0.388 0.391 0.309 0.128
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
45/100 3.04G 1.878 2.113 1.485 3 640: 100%|██████████| 71/71 [00:17<00:00, 3.95it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.77it/s]
all 324 324 0.401 0.384 0.346 0.145
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
46/100 3.04G 1.834 2.091 1.472 4 640: 100%|██████████| 71/71 [00:17<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.85it/s]
all 324 324 0.42 0.315 0.301 0.129
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
47/100 3.04G 1.853 2.056 1.467 6 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.81it/s]
all 324 324 0.369 0.347 0.305 0.123
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
48/100 3.04G 1.864 2.051 1.491 7 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.83it/s]
all 324 324 0.408 0.378 0.315 0.127
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
49/100 3.04G 1.803 2.103 1.445 3 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.64it/s]
all 324 324 0.421 0.383 0.322 0.137
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
50/100 3.04G 1.858 2.072 1.484 5 640: 100%|██████████| 71/71 [00:17<00:00, 4.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.78it/s]
all 324 324 0.41 0.395 0.308 0.122
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
51/100 3.04G 1.814 2.063 1.441 5 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.83it/s]
all 324 324 0.402 0.368 0.311 0.125
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
52/100 3.04G 1.82 2.018 1.432 7 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.81it/s]
all 324 324 0.38 0.397 0.311 0.125
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
53/100 3.04G 1.826 2.007 1.456 4 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.78it/s]
all 324 324 0.421 0.394 0.319 0.132
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
54/100 3.04G 1.789 2.014 1.44 6 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.80it/s]
all 324 324 0.367 0.388 0.296 0.123
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
55/100 3.04G 1.78 1.976 1.418 6 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.83it/s]
all 324 324 0.348 0.404 0.315 0.129
EarlyStopping: Training stopped early as no improvement observed in last 10 epochs. Best results observed at epoch 45, best model saved as best.pt.
To update EarlyStopping(patience=10) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.
55 epochs completed in 0.330 hours. Optimizer stripped from runs/detect/train41/weights/last.pt, 5.5MB Optimizer stripped from runs/detect/train41/weights/best.pt, 5.5MB Validating runs/detect/train41/weights/best.pt... Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB) YOLO11n summary (fused): 100 layers, 2,582,542 parameters, 0 gradients, 6.3 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:05<00:00, 1.90it/s]
all 324 324 0.402 0.384 0.345 0.144
BENIGN 174 174 0.428 0.328 0.34 0.145
MALIGNANT 150 150 0.377 0.44 0.351 0.144
Speed: 0.2ms preprocess, 3.2ms inference, 0.0ms loss, 3.1ms postprocess per image
Results saved to runs/detect/train41
ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([0, 1])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7fc6cbaf2680>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
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0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,
0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,
0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,
0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.9023, 0.9023, 0.88506, ..., 0, 0, 0],
[ 0.9, 0.9, 0.87333, ..., 0, 0, 0]]), 'Confidence', 'Recall']]
fitness: np.float64(0.1642880041285661)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.14487, 0.14351])
names: {0: 'BENIGN', 1: 'MALIGNANT'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.40246863379133063), 'metrics/recall(B)': np.float64(0.3837931034482759), 'metrics/mAP50(B)': np.float64(0.34517281147325807), 'metrics/mAP50-95(B)': np.float64(0.1441896922013781), 'fitness': np.float64(0.1642880041285661)}
save_dir: PosixPath('runs/detect/train41')
speed: {'preprocess': 0.20064869723110287, 'inference': 3.2458971082060426, 'loss': 0.002713595160547598, 'postprocess': 3.0774328610457387}
task: 'detect'
# Entrenamiento YOLOv11
!ls runs/detect/train41
/etc/bash.bashrc: line 5: export: `:/home/mperpinav/.local/lib/python3.10/site-packages/cv2/../../lib64:/usr/local/cuda-12.2/lib64': not a valid identifier args.yaml PR_curve.png val_batch0_labels.jpg confusion_matrix_normalized.png R_curve.png val_batch0_pred.jpg confusion_matrix.png results.csv val_batch1_labels.jpg F1_curve.png results.png val_batch1_pred.jpg labels_correlogram.jpg train_batch0.jpg val_batch2_labels.jpg labels.jpg train_batch1.jpg val_batch2_pred.jpg P_curve.png train_batch2.jpg weights
# Entrenamiento modelo mass YOLO v5
model_55 = YOLO("yolov5n.pt")
data_mass_path = os.path.join('final_dataset_yolo', 'data.yaml')
model_55.train(data=data_mass_path, epochs=100, patience = 10)
PRO TIP 💡 Replace 'model=yolov5n.pt' with new 'model=yolov5nu.pt'. YOLOv5 'u' models are trained with https://github.com/ultralytics/ultralytics and feature improved performance vs standard YOLOv5 models trained with https://github.com/ultralytics/yolov5. Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov5nu.pt to 'yolov5nu.pt'...
100%|██████████| 5.31M/5.31M [00:02<00:00, 2.07MB/s]
New https://pypi.org/project/ultralytics/8.3.126 available 😃 Update with 'pip install -U ultralytics' Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB) engine/trainer: task=detect, mode=train, model=yolov5n.pt, data=final_dataset_yolo/data.yaml, epochs=100, time=None, patience=10, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train43, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, cutmix=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train43 Overriding model.yaml nc=80 with nc=2 from n params module arguments 0 -1 1 1760 ultralytics.nn.modules.conv.Conv [3, 16, 6, 2, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 4800 ultralytics.nn.modules.block.C3 [32, 32, 1] 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 4 -1 2 29184 ultralytics.nn.modules.block.C3 [64, 64, 2] 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 6 -1 3 156928 ultralytics.nn.modules.block.C3 [128, 128, 3] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 296448 ultralytics.nn.modules.block.C3 [256, 256, 1] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 33024 ultralytics.nn.modules.conv.Conv [256, 128, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 13 -1 1 90880 ultralytics.nn.modules.block.C3 [256, 128, 1, False] 14 -1 1 8320 ultralytics.nn.modules.conv.Conv [128, 64, 1, 1] 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 16 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 17 -1 1 22912 ultralytics.nn.modules.block.C3 [128, 64, 1, False] 18 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 19 [-1, 14] 1 0 ultralytics.nn.modules.conv.Concat [1] 20 -1 1 74496 ultralytics.nn.modules.block.C3 [128, 128, 1, False] 21 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 22 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] 23 -1 1 296448 ultralytics.nn.modules.block.C3 [256, 256, 1, False] 24 [17, 20, 23] 1 751702 ultralytics.nn.modules.head.Detect [2, [64, 128, 256]] YOLOv5n summary: 153 layers, 2,508,854 parameters, 2,508,838 gradients, 7.2 GFLOPs Transferred 391/427 items from pretrained weights Freezing layer 'model.24.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ✅ train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 1028.2±295.5 MB/s, size: 82.0 KB)
train: Scanning /home/mperpinav/final_dataset_yolo/labels/train.cache... 1124 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1124/1124 [00:00<?, ?it/s]
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 490.8±390.7 MB/s, size: 73.0 KB)
val: Scanning /home/mperpinav/final_dataset_yolo/labels/val.cache... 324 images, 0 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:00<?, ?it/s]
Plotting labels to runs/detect/train43/labels.jpg... optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 69 weight(decay=0.0), 76 weight(decay=0.0005), 75 bias(decay=0.0) Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to runs/detect/train43 Starting training for 100 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/100 2.17G 2.345 5.011 1.756 6 640: 100%|██████████| 71/71 [00:19<00:00, 3.71it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.54it/s]
all 324 324 0.00167 0.499 0.0577 0.0169
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/100 2.44G 2.173 3.801 1.582 4 640: 100%|██████████| 71/71 [00:15<00:00, 4.65it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.07it/s]
all 324 324 0.233 0.167 0.103 0.036
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/100 2.45G 2.219 3.33 1.636 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.97it/s]
all 324 324 0.222 0.324 0.152 0.0582
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/100 2.46G 2.199 3.084 1.638 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.09it/s]
all 324 324 0.198 0.242 0.145 0.0539
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/100 2.47G 2.168 2.955 1.584 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.09it/s]
all 324 324 0.164 0.169 0.0897 0.0333
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/100 2.49G 2.147 2.815 1.639 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.11it/s]
all 324 324 0.252 0.307 0.171 0.0659
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/100 2.49G 2.152 2.769 1.629 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.07it/s]
all 324 324 0.282 0.29 0.172 0.0648
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/100 2.49G 2.081 2.729 1.611 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.10it/s]
all 324 324 0.272 0.231 0.174 0.0716
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/100 2.49G 2.045 2.654 1.553 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.09it/s]
all 324 324 0.313 0.294 0.208 0.077
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/100 2.49G 2.087 2.655 1.579 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.81it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.00it/s]
all 324 324 0.307 0.336 0.221 0.0887
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/100 2.49G 2.065 2.552 1.603 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.00it/s]
all 324 324 0.269 0.357 0.216 0.0834
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/100 2.51G 2.04 2.576 1.56 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.89it/s]
all 324 324 0.33 0.333 0.224 0.0878
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/100 2.51G 2.048 2.617 1.57 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.04it/s]
all 324 324 0.316 0.313 0.226 0.0933
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/100 2.54G 2.052 2.574 1.584 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.93it/s]
all 324 324 0.336 0.338 0.233 0.0899
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/100 2.55G 2.067 2.516 1.581 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.90it/s]
all 324 324 0.311 0.292 0.22 0.0816
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/100 2.56G 2.024 2.475 1.546 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.04it/s]
all 324 324 0.346 0.313 0.253 0.102
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/100 2.56G 2.051 2.507 1.566 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.01it/s]
all 324 324 0.325 0.264 0.213 0.0876
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/100 2.56G 2.006 2.505 1.574 9 640: 100%|██████████| 71/71 [00:14<00:00, 4.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.17it/s]
all 324 324 0.344 0.354 0.252 0.0977
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/100 2.56G 1.966 2.433 1.545 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.86it/s]
all 324 324 0.329 0.33 0.232 0.0898
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/100 2.56G 2 2.401 1.525 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.96it/s]
all 324 324 0.391 0.339 0.273 0.108
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/100 2.56G 1.997 2.489 1.549 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.06it/s]
all 324 324 0.355 0.354 0.266 0.108
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/100 2.56G 1.999 2.352 1.545 4 640: 100%|██████████| 71/71 [00:16<00:00, 4.31it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.62it/s]
all 324 324 0.379 0.369 0.284 0.106
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/100 2.56G 1.972 2.397 1.541 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.81it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.66it/s]
all 324 324 0.337 0.345 0.266 0.105
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/100 2.56G 1.972 2.422 1.567 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.11it/s]
all 324 324 0.349 0.376 0.267 0.109
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/100 2.56G 1.949 2.376 1.529 9 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.08it/s]
all 324 324 0.335 0.37 0.282 0.114
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/100 2.56G 1.916 2.349 1.478 10 640: 100%|██████████| 71/71 [00:15<00:00, 4.58it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.92it/s]
all 324 324 0.391 0.235 0.23 0.0953
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/100 2.56G 1.96 2.342 1.522 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.79it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.90it/s]
all 324 324 0.345 0.372 0.283 0.108
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/100 2.56G 1.939 2.355 1.512 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.06it/s]
all 324 324 0.422 0.26 0.225 0.0798
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/100 2.56G 1.947 2.381 1.545 9 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.11it/s]
all 324 324 0.306 0.346 0.254 0.104
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/100 2.56G 1.928 2.34 1.497 10 640: 100%|██████████| 71/71 [00:15<00:00, 4.70it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.12it/s]
all 324 324 0.356 0.451 0.291 0.109
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/100 2.56G 1.921 2.243 1.513 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.02it/s]
all 324 324 0.373 0.378 0.302 0.126
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/100 2.56G 1.922 2.264 1.469 5 640: 100%|██████████| 71/71 [00:15<00:00, 4.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.11it/s]
all 324 324 0.373 0.387 0.286 0.118
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/100 2.56G 1.907 2.212 1.497 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.08it/s]
all 324 324 0.404 0.331 0.3 0.123
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/100 2.56G 1.906 2.299 1.477 6 640: 100%|██████████| 71/71 [00:15<00:00, 4.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.15it/s]
all 324 324 0.374 0.363 0.28 0.113
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/100 2.56G 1.92 2.176 1.517 4 640: 100%|██████████| 71/71 [00:16<00:00, 4.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.52it/s]
all 324 324 0.325 0.352 0.236 0.0952
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/100 2.56G 1.931 2.207 1.523 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.02it/s]
all 324 324 0.379 0.368 0.276 0.113
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/100 2.56G 1.898 2.218 1.478 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.81it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.14it/s]
all 324 324 0.305 0.416 0.266 0.111
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/100 2.56G 1.94 2.288 1.509 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.08it/s]
all 324 324 0.406 0.389 0.294 0.114
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
39/100 2.56G 1.865 2.213 1.472 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.12it/s]
all 324 324 0.341 0.313 0.247 0.0997
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
40/100 2.56G 1.928 2.225 1.5 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.07it/s]
all 324 324 0.448 0.384 0.306 0.127
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
41/100 2.56G 1.871 2.133 1.47 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.13it/s]
all 324 324 0.335 0.364 0.273 0.115
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
42/100 2.56G 1.925 2.152 1.523 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.96it/s]
all 324 324 0.384 0.357 0.297 0.109
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
43/100 2.56G 1.849 2.163 1.443 9 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.11it/s]
all 324 324 0.364 0.378 0.309 0.125
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
44/100 2.56G 1.861 2.168 1.456 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.75it/s]
all 324 324 0.406 0.401 0.304 0.119
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
45/100 2.56G 1.857 2.118 1.48 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.75it/s]
all 324 324 0.353 0.372 0.279 0.107
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
46/100 2.56G 1.82 2.114 1.456 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.10it/s]
all 324 324 0.395 0.365 0.304 0.118
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
47/100 2.56G 1.832 2.031 1.45 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.71it/s]
all 324 324 0.35 0.413 0.278 0.115
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
48/100 2.56G 1.831 2.017 1.473 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.08it/s]
all 324 324 0.363 0.433 0.308 0.128
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
49/100 2.56G 1.807 2.055 1.434 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.15it/s]
all 324 324 0.407 0.361 0.284 0.119
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
50/100 2.56G 1.828 2.052 1.461 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.14it/s]
all 324 324 0.477 0.388 0.353 0.135
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
51/100 2.56G 1.81 2.024 1.432 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.15it/s]
all 324 324 0.35 0.369 0.3 0.12
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
52/100 2.56G 1.82 2.021 1.42 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.15it/s]
all 324 324 0.441 0.362 0.282 0.114
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
53/100 2.56G 1.798 2.01 1.438 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.12it/s]
all 324 324 0.403 0.422 0.322 0.127
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
54/100 2.56G 1.8 2.003 1.431 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.90it/s]
all 324 324 0.425 0.349 0.317 0.124
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
55/100 2.56G 1.761 1.918 1.397 6 640: 100%|██████████| 71/71 [00:15<00:00, 4.64it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.92it/s]
all 324 324 0.41 0.381 0.309 0.121
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
56/100 2.56G 1.751 1.954 1.412 7 640: 100%|██████████| 71/71 [00:15<00:00, 4.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.87it/s]
all 324 324 0.448 0.343 0.319 0.132
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
57/100 2.56G 1.811 1.971 1.43 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.17it/s]
all 324 324 0.364 0.398 0.322 0.134
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
58/100 2.56G 1.772 1.914 1.419 6 640: 100%|██████████| 71/71 [00:16<00:00, 4.26it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.12it/s]
all 324 324 0.376 0.354 0.294 0.12
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
59/100 2.56G 1.749 1.949 1.43 4 640: 100%|██████████| 71/71 [00:15<00:00, 4.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.18it/s]
all 324 324 0.386 0.35 0.308 0.122
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
60/100 2.56G 1.764 1.882 1.404 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.16it/s]
all 324 324 0.402 0.372 0.325 0.128
EarlyStopping: Training stopped early as no improvement observed in last 10 epochs. Best results observed at epoch 50, best model saved as best.pt.
To update EarlyStopping(patience=10) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.
60 epochs completed in 0.307 hours. Optimizer stripped from runs/detect/train43/weights/last.pt, 5.3MB Optimizer stripped from runs/detect/train43/weights/best.pt, 5.3MB Validating runs/detect/train43/weights/best.pt... Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB) YOLOv5n summary (fused): 84 layers, 2,503,334 parameters, 0 gradients, 7.1 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.25it/s]
all 324 324 0.477 0.388 0.353 0.135
BENIGN 174 174 0.481 0.356 0.355 0.143
MALIGNANT 150 150 0.473 0.42 0.351 0.126
Speed: 0.2ms preprocess, 2.5ms inference, 0.0ms loss, 2.0ms postprocess per image
Results saved to runs/detect/train43
ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([0, 1])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7fba8f058e20>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
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0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,
0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,
0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,
0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,
0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,
0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,
0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,
0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.89655, 0.89655, 0.86782, ..., 0, 0, 0],
[ 0.88667, 0.88667, 0.82667, ..., 0, 0, 0]]), 'Confidence', 'Recall']]
fitness: np.float64(0.15642811767360462)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.14343, 0.12576])
names: {0: 'BENIGN', 1: 'MALIGNANT'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.47680574727758174), 'metrics/recall(B)': np.float64(0.3881609195402299), 'metrics/mAP50(B)': np.float64(0.352938189824785), 'metrics/mAP50-95(B)': np.float64(0.13459366521236235), 'fitness': np.float64(0.15642811767360462)}
save_dir: PosixPath('runs/detect/train43')
speed: {'preprocess': 0.1829868544720941, 'inference': 2.477857427970495, 'loss': 0.006605914138533451, 'postprocess': 2.011933294987237}
task: 'detect'
model = YOLO('yolov8n.pt')
data_mass_path = os.path.join('final_dataset_yolo', 'data.yaml')
model.train(data=data_mass_path, epochs=120, patience = 45)
New https://pypi.org/project/ultralytics/8.3.127 available 😃 Update with 'pip install -U ultralytics' Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB) engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=final_dataset_yolo/data.yaml, epochs=120, time=None, patience=45, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train45, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, cutmix=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train45 Overriding model.yaml nc=80 with nc=2 from n params module arguments 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] 22 [15, 18, 21] 1 751702 ultralytics.nn.modules.head.Detect [2, [64, 128, 256]] Model summary: 129 layers, 3,011,238 parameters, 3,011,222 gradients, 8.2 GFLOPs Transferred 319/355 items from pretrained weights Freezing layer 'model.22.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ✅ train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 927.2±147.2 MB/s, size: 82.0 KB)
train: Scanning /home/mperpinav/final_dataset_yolo/labels/train.cache... 1124 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1124/1124 [00:00<?, ?it/s]
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 298.4±85.0 MB/s, size: 73.0 KB)
val: Scanning /home/mperpinav/final_dataset_yolo/labels/val.cache... 324 images, 0 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:00<?, ?it/s]
Plotting labels to runs/detect/train45/labels.jpg...
df.head()
| epoch | time | train/box_loss | train/cls_loss | train/dfl_loss | metrics/precision(B) | metrics/recall(B) | metrics/mAP50(B) | metrics/mAP50-95(B) | val/box_loss | val/cls_loss | val/dfl_loss | lr/pg0 | lr/pg1 | lr/pg2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 31.7220 | 2.34783 | 4.97834 | 1.71379 | 0.00115 | 0.34609 | 0.03220 | 0.01191 | 2.08884 | 3.75067 | 1.58988 | 0.000548 | 0.000548 | 0.000548 |
| 1 | 2 | 49.8062 | 2.16216 | 3.86718 | 1.59476 | 0.16575 | 0.19092 | 0.09857 | 0.03164 | 2.25879 | 3.39720 | 1.69511 | 0.001094 | 0.001094 | 0.001094 |
| 2 | 3 | 67.4871 | 2.21181 | 3.39654 | 1.62246 | 0.23572 | 0.26977 | 0.15420 | 0.05925 | 2.12477 | 2.81986 | 1.68035 | 0.001632 | 0.001632 | 0.001632 |
| 3 | 4 | 85.3997 | 2.17201 | 3.04748 | 1.59152 | 0.22115 | 0.24264 | 0.15074 | 0.06051 | 2.07679 | 2.78117 | 1.71249 | 0.001626 | 0.001626 | 0.001626 |
| 4 | 5 | 103.0380 | 2.16188 | 2.92345 | 1.63255 | 0.29474 | 0.29575 | 0.15845 | 0.06215 | 2.01306 | 2.65899 | 1.60202 | 0.001612 | 0.001612 | 0.001612 |
model_11 = YOLO("yolo11n.pt")
data_mass_path = os.path.join('final_dataset_yolo', 'data.yaml')
model_11.train(data=data_mass_path, epochs=120, patience = 45)
New https://pypi.org/project/ultralytics/8.3.127 available 😃 Update with 'pip install -U ultralytics' Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB) engine/trainer: task=detect, mode=train, model=yolo11n.pt, data=final_dataset_yolo/data.yaml, epochs=120, time=None, patience=45, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train53, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, cutmix=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train53 Overriding model.yaml nc=80 with nc=2 from n params module arguments 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25] 3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] 5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False] 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False] 17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] 19 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False] 20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] 22 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True] 23 [16, 19, 22] 1 431062 ultralytics.nn.modules.head.Detect [2, [64, 128, 256]] YOLO11n summary: 181 layers, 2,590,230 parameters, 2,590,214 gradients, 6.4 GFLOPs Transferred 448/499 items from pretrained weights Freezing layer 'model.23.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ✅ train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 952.6±260.2 MB/s, size: 82.0 KB)
train: Scanning /home/mperpinav/final_dataset_yolo/labels/train.cache... 1124 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1124/1124 [00:00<?, ?it/s]
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 408.7±449.0 MB/s, size: 73.0 KB)
val: Scanning /home/mperpinav/final_dataset_yolo/labels/val.cache... 324 images, 0 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:00<?, ?it/s]
Plotting labels to runs/detect/train53/labels.jpg... optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0) Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to runs/detect/train53 Starting training for 120 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/120 2.48G 2.357 5.12 1.706 6 640: 100%|██████████| 71/71 [00:19<00:00, 3.62it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.36it/s]
all 324 324 0.486 0.0157 0.0921 0.0268
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/120 2.75G 2.196 4.018 1.607 4 640: 100%|██████████| 71/71 [00:18<00:00, 3.92it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.36it/s]
all 324 324 0.155 0.192 0.0823 0.0263
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/120 2.77G 2.214 3.529 1.644 8 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.55it/s]
all 324 324 0.128 0.138 0.0715 0.0271
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/120 2.78G 2.171 3.125 1.621 8 640: 100%|██████████| 71/71 [00:17<00:00, 4.04it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.47it/s]
all 324 324 0.227 0.174 0.1 0.0356
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/120 2.79G 2.198 2.976 1.629 7 640: 100%|██████████| 71/71 [00:17<00:00, 4.05it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.66it/s]
all 324 324 0.202 0.251 0.14 0.052
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/120 2.81G 2.123 2.812 1.604 5 640: 100%|██████████| 71/71 [00:17<00:00, 4.04it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.79it/s]
all 324 324 0.265 0.271 0.167 0.0657
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/120 2.81G 2.123 2.722 1.605 3 640: 100%|██████████| 71/71 [00:17<00:00, 4.04it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.86it/s]
all 324 324 0.271 0.242 0.137 0.0552
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/120 2.81G 2.106 2.733 1.636 6 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.69it/s]
all 324 324 0.212 0.263 0.129 0.0533
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/120 2.81G 2.052 2.608 1.578 7 640: 100%|██████████| 71/71 [00:17<00:00, 4.04it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.72it/s]
all 324 324 0.283 0.342 0.204 0.0787
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/120 2.81G 2.085 2.636 1.6 6 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.58it/s]
all 324 324 0.359 0.307 0.22 0.0873
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/120 2.81G 2.078 2.557 1.578 3 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.86it/s]
all 324 324 0.283 0.337 0.207 0.0851
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/120 2.83G 2.041 2.578 1.581 6 640: 100%|██████████| 71/71 [00:17<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.59it/s]
all 324 324 0.303 0.296 0.216 0.0856
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/120 2.83G 2.054 2.612 1.576 4 640: 100%|██████████| 71/71 [00:18<00:00, 3.86it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.60it/s]
all 324 324 0.313 0.313 0.241 0.104
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/120 2.83G 2.055 2.542 1.603 5 640: 100%|██████████| 71/71 [00:17<00:00, 3.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.79it/s]
all 324 324 0.351 0.329 0.253 0.101
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/120 2.83G 2.058 2.502 1.601 4 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.64it/s]
all 324 324 0.282 0.313 0.242 0.0957
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/120 2.83G 2.028 2.48 1.563 5 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.78it/s]
all 324 324 0.337 0.304 0.242 0.0986
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/120 2.83G 2.066 2.538 1.567 6 640: 100%|██████████| 71/71 [00:17<00:00, 4.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.62it/s]
all 324 324 0.306 0.3 0.19 0.0778
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/120 2.83G 2.002 2.48 1.57 9 640: 100%|██████████| 71/71 [00:17<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.87it/s]
all 324 324 0.406 0.308 0.279 0.112
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/120 2.83G 1.974 2.481 1.53 8 640: 100%|██████████| 71/71 [00:17<00:00, 4.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.76it/s]
all 324 324 0.345 0.372 0.26 0.105
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/120 2.83G 1.988 2.405 1.523 4 640: 100%|██████████| 71/71 [00:17<00:00, 3.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.80it/s]
all 324 324 0.375 0.382 0.294 0.121
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/120 2.83G 2.025 2.47 1.571 5 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.66it/s]
all 324 324 0.279 0.323 0.235 0.094
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/120 2.83G 2.006 2.33 1.539 4 640: 100%|██████████| 71/71 [00:18<00:00, 3.94it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.78it/s]
all 324 324 0.37 0.341 0.284 0.108
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/120 2.83G 2.011 2.43 1.565 5 640: 100%|██████████| 71/71 [00:17<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.56it/s]
all 324 324 0.267 0.24 0.164 0.0661
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/120 2.83G 1.976 2.457 1.583 5 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.67it/s]
all 324 324 0.379 0.297 0.243 0.1
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/120 2.83G 1.938 2.363 1.512 9 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.84it/s]
all 324 324 0.35 0.359 0.271 0.11
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/120 2.83G 1.931 2.34 1.502 10 640: 100%|██████████| 71/71 [00:17<00:00, 3.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.83it/s]
all 324 324 0.324 0.352 0.277 0.115
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/120 2.83G 1.962 2.368 1.513 8 640: 100%|██████████| 71/71 [00:17<00:00, 4.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.84it/s]
all 324 324 0.365 0.285 0.286 0.119
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/120 2.83G 1.948 2.356 1.535 3 640: 100%|██████████| 71/71 [00:17<00:00, 4.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.85it/s]
all 324 324 0.288 0.202 0.206 0.0762
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/120 2.83G 1.944 2.345 1.537 9 640: 100%|██████████| 71/71 [00:17<00:00, 3.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.81it/s]
all 324 324 0.364 0.365 0.283 0.111
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/120 2.83G 1.943 2.357 1.527 10 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.78it/s]
all 324 324 0.357 0.351 0.302 0.122
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/120 2.83G 1.951 2.291 1.527 7 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.87it/s]
all 324 324 0.325 0.346 0.284 0.11
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/120 2.84G 1.922 2.28 1.479 5 640: 100%|██████████| 71/71 [00:17<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.80it/s]
all 324 324 0.393 0.311 0.279 0.117
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/120 2.84G 1.907 2.194 1.502 3 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.83it/s]
all 324 324 0.429 0.359 0.3 0.12
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/120 2.84G 1.939 2.319 1.511 6 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.82it/s]
all 324 324 0.365 0.361 0.275 0.11
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/120 2.84G 1.905 2.215 1.519 4 640: 100%|██████████| 71/71 [00:17<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.62it/s]
all 324 324 0.346 0.377 0.304 0.12
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/120 2.84G 1.939 2.243 1.525 8 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.73it/s]
all 324 324 0.34 0.308 0.244 0.1
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/120 2.84G 1.885 2.259 1.483 8 640: 100%|██████████| 71/71 [00:17<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.43it/s]
all 324 324 0.313 0.446 0.311 0.128
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/120 2.84G 1.956 2.282 1.525 7 640: 100%|██████████| 71/71 [00:17<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.78it/s]
all 324 324 0.386 0.369 0.293 0.124
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
39/120 2.84G 1.884 2.232 1.474 7 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.37it/s]
all 324 324 0.416 0.321 0.306 0.123
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
40/120 2.84G 1.949 2.226 1.52 6 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.72it/s]
all 324 324 0.393 0.394 0.328 0.133
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
41/120 2.84G 1.904 2.152 1.498 6 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.86it/s]
all 324 324 0.376 0.362 0.316 0.13
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
42/120 2.84G 1.94 2.22 1.521 5 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.84it/s]
all 324 324 0.374 0.356 0.311 0.126
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
43/120 2.84G 1.888 2.211 1.474 9 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.68it/s]
all 324 324 0.406 0.384 0.314 0.13
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
44/120 2.84G 1.897 2.19 1.485 7 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.81it/s]
all 324 324 0.308 0.34 0.219 0.0863
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
45/120 2.84G 1.892 2.131 1.492 3 640: 100%|██████████| 71/71 [00:18<00:00, 3.90it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.72it/s]
all 324 324 0.35 0.451 0.327 0.137
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
46/120 2.84G 1.82 2.137 1.465 4 640: 100%|██████████| 71/71 [00:17<00:00, 4.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.57it/s]
all 324 324 0.46 0.342 0.316 0.134
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
47/120 2.84G 1.844 2.072 1.447 6 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.71it/s]
all 324 324 0.394 0.36 0.3 0.117
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
48/120 2.84G 1.853 2.095 1.493 7 640: 100%|██████████| 71/71 [00:17<00:00, 4.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.71it/s]
all 324 324 0.324 0.364 0.305 0.126
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
49/120 2.84G 1.807 2.122 1.451 3 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.82it/s]
all 324 324 0.409 0.385 0.321 0.132
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
50/120 2.84G 1.838 2.091 1.467 5 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.70it/s]
all 324 324 0.391 0.417 0.322 0.134
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
51/120 2.84G 1.817 2.109 1.453 5 640: 100%|██████████| 71/71 [00:17<00:00, 4.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.80it/s]
all 324 324 0.37 0.38 0.303 0.126
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
52/120 2.84G 1.827 2.07 1.434 7 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.81it/s]
all 324 324 0.331 0.44 0.327 0.13
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
53/120 2.84G 1.851 2.068 1.475 4 640: 100%|██████████| 71/71 [00:17<00:00, 4.04it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.75it/s]
all 324 324 0.36 0.413 0.335 0.133
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
54/120 2.84G 1.835 2.106 1.471 6 640: 100%|██████████| 71/71 [00:17<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.46it/s]
all 324 324 0.375 0.377 0.313 0.13
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
55/120 2.84G 1.79 1.993 1.42 6 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.76it/s]
all 324 324 0.359 0.397 0.316 0.124
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
56/120 2.84G 1.806 2.016 1.445 7 640: 100%|██████████| 71/71 [00:17<00:00, 4.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.84it/s]
all 324 324 0.36 0.373 0.299 0.12
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
57/120 2.84G 1.824 2.073 1.447 4 640: 100%|██████████| 71/71 [00:17<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.84it/s]
all 324 324 0.358 0.379 0.309 0.128
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
58/120 2.84G 1.803 1.974 1.442 6 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.79it/s]
all 324 324 0.42 0.439 0.351 0.135
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
59/120 2.84G 1.811 2.006 1.466 4 640: 100%|██████████| 71/71 [00:17<00:00, 4.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.86it/s]
all 324 324 0.385 0.414 0.319 0.128
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
60/120 2.84G 1.81 1.959 1.442 4 640: 100%|██████████| 71/71 [00:17<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.77it/s]
all 324 324 0.408 0.367 0.314 0.126
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
61/120 2.84G 1.804 2.032 1.455 8 640: 100%|██████████| 71/71 [00:17<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.68it/s]
all 324 324 0.484 0.38 0.344 0.141
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
62/120 2.84G 1.799 1.983 1.425 9 640: 100%|██████████| 71/71 [00:17<00:00, 3.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.76it/s]
all 324 324 0.449 0.384 0.351 0.146
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
63/120 2.84G 1.8 1.984 1.447 4 640: 100%|██████████| 71/71 [00:17<00:00, 4.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.73it/s]
all 324 324 0.39 0.389 0.306 0.118
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
64/120 2.84G 1.754 1.92 1.407 3 640: 100%|██████████| 71/71 [00:17<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.61it/s]
all 324 324 0.385 0.376 0.322 0.134
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
65/120 2.84G 1.748 1.953 1.439 5 640: 100%|██████████| 71/71 [00:17<00:00, 3.95it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.90it/s]
all 324 324 0.429 0.38 0.346 0.145
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
66/120 2.84G 1.766 1.945 1.417 3 640: 100%|██████████| 71/71 [00:18<00:00, 3.94it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.84it/s]
all 324 324 0.432 0.382 0.328 0.13
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
67/120 2.84G 1.781 1.945 1.427 10 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.90it/s]
all 324 324 0.358 0.366 0.278 0.113
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
68/120 2.84G 1.724 1.893 1.395 5 640: 100%|██████████| 71/71 [00:17<00:00, 3.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.66it/s]
all 324 324 0.384 0.417 0.325 0.133
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
69/120 2.84G 1.75 1.939 1.403 5 640: 100%|██████████| 71/71 [00:17<00:00, 3.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.77it/s]
all 324 324 0.463 0.392 0.352 0.141
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
70/120 2.84G 1.736 1.908 1.418 6 640: 100%|██████████| 71/71 [00:19<00:00, 3.70it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.49it/s]
all 324 324 0.411 0.437 0.357 0.144
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
71/120 2.84G 1.763 1.977 1.433 3 640: 100%|██████████| 71/71 [00:18<00:00, 3.83it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.86it/s]
all 324 324 0.406 0.39 0.333 0.134
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
72/120 2.84G 1.724 1.882 1.395 7 640: 100%|██████████| 71/71 [00:20<00:00, 3.50it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.80it/s]
all 324 324 0.391 0.374 0.31 0.126
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
73/120 2.84G 1.729 1.893 1.389 7 640: 100%|██████████| 71/71 [00:18<00:00, 3.89it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.86it/s]
all 324 324 0.406 0.374 0.329 0.135
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
74/120 2.84G 1.731 1.845 1.404 4 640: 100%|██████████| 71/71 [00:18<00:00, 3.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.31it/s]
all 324 324 0.407 0.386 0.309 0.117
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
75/120 2.84G 1.705 1.874 1.385 5 640: 100%|██████████| 71/71 [00:18<00:00, 3.86it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.88it/s]
all 324 324 0.39 0.423 0.334 0.133
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
76/120 2.84G 1.696 1.843 1.386 5 640: 100%|██████████| 71/71 [00:20<00:00, 3.51it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.49it/s]
all 324 324 0.44 0.414 0.328 0.138
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
77/120 2.84G 1.714 1.898 1.386 5 640: 100%|██████████| 71/71 [00:19<00:00, 3.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.72it/s]
all 324 324 0.466 0.365 0.342 0.14
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
78/120 2.84G 1.697 1.829 1.389 9 640: 100%|██████████| 71/71 [00:19<00:00, 3.61it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.67it/s]
all 324 324 0.421 0.352 0.321 0.126
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
79/120 2.84G 1.704 1.79 1.391 3 640: 100%|██████████| 71/71 [00:18<00:00, 3.81it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.73it/s]
all 324 324 0.4 0.368 0.319 0.127
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
80/120 2.84G 1.705 1.809 1.379 8 640: 100%|██████████| 71/71 [00:18<00:00, 3.83it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.83it/s]
all 324 324 0.451 0.375 0.337 0.132
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
81/120 2.84G 1.675 1.79 1.386 4 640: 100%|██████████| 71/71 [00:18<00:00, 3.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 2.98it/s]
all 324 324 0.446 0.37 0.338 0.133
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
82/120 2.84G 1.671 1.78 1.35 2 640: 100%|██████████| 71/71 [00:18<00:00, 3.85it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.51it/s]
all 324 324 0.433 0.372 0.326 0.135
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
83/120 2.84G 1.657 1.79 1.362 6 640: 100%|██████████| 71/71 [00:19<00:00, 3.66it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.82it/s]
all 324 324 0.4 0.449 0.337 0.135
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
84/120 2.84G 1.675 1.769 1.357 9 640: 100%|██████████| 71/71 [00:19<00:00, 3.68it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.78it/s]
all 324 324 0.419 0.364 0.334 0.135
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
85/120 2.84G 1.645 1.737 1.344 9 640: 100%|██████████| 71/71 [00:18<00:00, 3.82it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.52it/s]
all 324 324 0.397 0.378 0.314 0.129
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
86/120 2.84G 1.665 1.743 1.365 3 640: 100%|██████████| 71/71 [00:18<00:00, 3.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.73it/s]
all 324 324 0.428 0.415 0.344 0.141
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
87/120 2.84G 1.659 1.732 1.371 6 640: 100%|██████████| 71/71 [00:18<00:00, 3.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.73it/s]
all 324 324 0.407 0.415 0.344 0.143
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
88/120 2.84G 1.648 1.723 1.352 7 640: 100%|██████████| 71/71 [00:19<00:00, 3.67it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.56it/s]
all 324 324 0.367 0.41 0.311 0.128
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
89/120 2.84G 1.662 1.687 1.355 9 640: 100%|██████████| 71/71 [00:19<00:00, 3.56it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.47it/s]
all 324 324 0.473 0.381 0.327 0.132
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
90/120 2.84G 1.655 1.668 1.374 5 640: 100%|██████████| 71/71 [00:19<00:00, 3.71it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.81it/s]
all 324 324 0.442 0.393 0.338 0.135
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
91/120 2.84G 1.618 1.707 1.33 4 640: 100%|██████████| 71/71 [00:18<00:00, 3.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.36it/s]
all 324 324 0.416 0.41 0.352 0.141
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
92/120 2.84G 1.645 1.632 1.356 9 640: 100%|██████████| 71/71 [00:19<00:00, 3.65it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.12it/s]
all 324 324 0.39 0.38 0.317 0.128
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
93/120 2.84G 1.622 1.672 1.338 5 640: 100%|██████████| 71/71 [00:18<00:00, 3.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.79it/s]
all 324 324 0.413 0.355 0.31 0.127
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
94/120 2.84G 1.589 1.587 1.331 3 640: 100%|██████████| 71/71 [00:20<00:00, 3.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.73it/s]
all 324 324 0.395 0.435 0.328 0.129
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
95/120 2.84G 1.636 1.622 1.339 9 640: 100%|██████████| 71/71 [00:18<00:00, 3.85it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.55it/s]
all 324 324 0.451 0.391 0.344 0.132
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
96/120 2.84G 1.6 1.624 1.341 7 640: 100%|██████████| 71/71 [00:20<00:00, 3.49it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.54it/s]
all 324 324 0.446 0.357 0.335 0.137
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
97/120 2.84G 1.598 1.644 1.33 8 640: 100%|██████████| 71/71 [00:18<00:00, 3.79it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.27it/s]
all 324 324 0.395 0.44 0.343 0.135
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
98/120 2.84G 1.601 1.646 1.326 4 640: 100%|██████████| 71/71 [00:19<00:00, 3.57it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.78it/s]
all 324 324 0.395 0.404 0.321 0.13
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
99/120 2.84G 1.589 1.629 1.323 6 640: 100%|██████████| 71/71 [00:19<00:00, 3.56it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.44it/s]
all 324 324 0.42 0.418 0.339 0.137
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
100/120 2.84G 1.607 1.624 1.323 10 640: 100%|██████████| 71/71 [00:19<00:00, 3.56it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.53it/s]
all 324 324 0.399 0.405 0.341 0.141
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
101/120 2.84G 1.56 1.6 1.311 8 640: 100%|██████████| 71/71 [00:18<00:00, 3.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.65it/s]
all 324 324 0.444 0.438 0.356 0.141
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
102/120 2.84G 1.562 1.564 1.325 7 640: 100%|██████████| 71/71 [00:19<00:00, 3.64it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.49it/s]
all 324 324 0.416 0.445 0.348 0.135
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
103/120 2.84G 1.53 1.529 1.301 8 640: 100%|██████████| 71/71 [00:18<00:00, 3.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.61it/s]
all 324 324 0.432 0.391 0.342 0.139
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
104/120 2.84G 1.558 1.551 1.302 4 640: 100%|██████████| 71/71 [00:19<00:00, 3.57it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.02it/s]
all 324 324 0.507 0.308 0.334 0.137
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
105/120 2.84G 1.542 1.516 1.313 7 640: 100%|██████████| 71/71 [00:19<00:00, 3.58it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.46it/s]
all 324 324 0.406 0.384 0.332 0.137
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
106/120 2.84G 1.546 1.496 1.318 3 640: 100%|██████████| 71/71 [00:20<00:00, 3.48it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.65it/s]
all 324 324 0.426 0.366 0.341 0.133
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
107/120 2.84G 1.549 1.527 1.313 5 640: 100%|██████████| 71/71 [00:18<00:00, 3.86it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.43it/s]
all 324 324 0.393 0.407 0.341 0.138
EarlyStopping: Training stopped early as no improvement observed in last 45 epochs. Best results observed at epoch 62, best model saved as best.pt.
To update EarlyStopping(patience=45) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.
107 epochs completed in 0.655 hours. Optimizer stripped from runs/detect/train53/weights/last.pt, 5.5MB Optimizer stripped from runs/detect/train53/weights/best.pt, 5.5MB Validating runs/detect/train53/weights/best.pt... Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB) YOLO11n summary (fused): 100 layers, 2,582,542 parameters, 0 gradients, 6.3 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:04<00:00, 2.53it/s]
all 324 324 0.444 0.393 0.353 0.147
BENIGN 174 174 0.512 0.332 0.355 0.147
MALIGNANT 150 150 0.377 0.453 0.351 0.146
Speed: 0.2ms preprocess, 3.1ms inference, 0.0ms loss, 3.3ms postprocess per image
Results saved to runs/detect/train53
ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([0, 1])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7f7f8d0841c0>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
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0.72072, 0.72172, 0.72272, 0.72372, 0.72472, 0.72573, 0.72673, 0.72773, 0.72873, 0.72973, 0.73073, 0.73173, 0.73273, 0.73373, 0.73473, 0.73574, 0.73674, 0.73774, 0.73874, 0.73974, 0.74074, 0.74174, 0.74274, 0.74374,
0.74474, 0.74575, 0.74675, 0.74775, 0.74875, 0.74975, 0.75075, 0.75175, 0.75275, 0.75375, 0.75475, 0.75576, 0.75676, 0.75776, 0.75876, 0.75976, 0.76076, 0.76176, 0.76276, 0.76376, 0.76476, 0.76577, 0.76677, 0.76777,
0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.77778, 0.77878, 0.77978, 0.78078, 0.78178, 0.78278, 0.78378, 0.78478, 0.78579, 0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,
0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,
0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,
0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,
0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,
0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,
0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,
0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,
0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,
0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.89655, 0.89655, 0.87356, ..., 0, 0, 0],
[ 0.90667, 0.90667, 0.88, ..., 0, 0, 0]]), 'Confidence', 'Recall']]
fitness: np.float64(0.16718371543735006)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.1473, 0.14576])
names: {0: 'BENIGN', 1: 'MALIGNANT'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.44446430853375707), 'metrics/recall(B)': np.float64(0.3927243910577244), 'metrics/mAP50(B)': np.float64(0.3530777145213868), 'metrics/mAP50-95(B)': np.float64(0.14652882665023487), 'fitness': np.float64(0.16718371543735006)}
save_dir: PosixPath('runs/detect/train53')
speed: {'preprocess': 0.1796138915520759, 'inference': 3.140773724787581, 'loss': 0.0019342535071902804, 'postprocess': 3.310438906658947}
task: 'detect'
# Entrenamiento modelo mass YOLO v5
model_55 = YOLO("yolov5n.pt")
data_mass_path = os.path.join('final_dataset_yolo', 'data.yaml')
model_55.train(data=data_mass_path, epochs=120, patience = 45)
PRO TIP 💡 Replace 'model=yolov5n.pt' with new 'model=yolov5nu.pt'. YOLOv5 'u' models are trained with https://github.com/ultralytics/ultralytics and feature improved performance vs standard YOLOv5 models trained with https://github.com/ultralytics/yolov5. New https://pypi.org/project/ultralytics/8.3.127 available 😃 Update with 'pip install -U ultralytics' Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB) engine/trainer: task=detect, mode=train, model=yolov5n.pt, data=final_dataset_yolo/data.yaml, epochs=120, time=None, patience=45, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train51, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, cutmix=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train51 Overriding model.yaml nc=80 with nc=2 from n params module arguments 0 -1 1 1760 ultralytics.nn.modules.conv.Conv [3, 16, 6, 2, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 4800 ultralytics.nn.modules.block.C3 [32, 32, 1] 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 4 -1 2 29184 ultralytics.nn.modules.block.C3 [64, 64, 2] 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 6 -1 3 156928 ultralytics.nn.modules.block.C3 [128, 128, 3] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 296448 ultralytics.nn.modules.block.C3 [256, 256, 1] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 33024 ultralytics.nn.modules.conv.Conv [256, 128, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 13 -1 1 90880 ultralytics.nn.modules.block.C3 [256, 128, 1, False] 14 -1 1 8320 ultralytics.nn.modules.conv.Conv [128, 64, 1, 1] 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 16 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 17 -1 1 22912 ultralytics.nn.modules.block.C3 [128, 64, 1, False] 18 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 19 [-1, 14] 1 0 ultralytics.nn.modules.conv.Concat [1] 20 -1 1 74496 ultralytics.nn.modules.block.C3 [128, 128, 1, False] 21 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 22 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] 23 -1 1 296448 ultralytics.nn.modules.block.C3 [256, 256, 1, False] 24 [17, 20, 23] 1 751702 ultralytics.nn.modules.head.Detect [2, [64, 128, 256]] YOLOv5n summary: 153 layers, 2,508,854 parameters, 2,508,838 gradients, 7.2 GFLOPs Transferred 391/427 items from pretrained weights Freezing layer 'model.24.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ✅ train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 798.5±219.5 MB/s, size: 82.0 KB)
train: Scanning /home/mperpinav/final_dataset_yolo/labels/train.cache... 1124 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1124/1124 [00:00<?, ?it/s]
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 460.2±451.0 MB/s, size: 73.0 KB)
val: Scanning /home/mperpinav/final_dataset_yolo/labels/val.cache... 324 images, 0 backgrounds, 0 corrupt: 100%|██████████| 324/324 [00:00<?, ?it/s]
Plotting labels to runs/detect/train51/labels.jpg... optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 69 weight(decay=0.0), 76 weight(decay=0.0005), 75 bias(decay=0.0) Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to runs/detect/train51 Starting training for 120 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/120 2.17G 2.345 5.011 1.756 6 640: 100%|██████████| 71/71 [00:17<00:00, 4.12it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.44it/s]
all 324 324 0.00167 0.499 0.0577 0.0169
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/120 2.44G 2.194 3.771 1.585 4 640: 100%|██████████| 71/71 [00:15<00:00, 4.60it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.99it/s]
all 324 324 0.158 0.211 0.0951 0.0339
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/120 2.44G 2.201 3.322 1.645 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.81it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.94it/s]
all 324 324 0.181 0.339 0.141 0.0523
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/120 2.45G 2.131 3.042 1.623 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.12it/s]
all 324 324 0.248 0.269 0.149 0.0594
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/120 2.46G 2.168 2.882 1.625 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.00it/s]
all 324 324 0.195 0.221 0.108 0.0423
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/120 2.48G 2.107 2.79 1.616 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.04it/s]
all 324 324 0.223 0.271 0.171 0.0656
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/120 2.49G 2.094 2.73 1.627 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.12it/s]
all 324 324 0.277 0.199 0.13 0.0476
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/120 2.51G 2.11 2.714 1.637 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.90it/s]
all 324 324 0.236 0.136 0.107 0.0459
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/120 2.51G 2.059 2.619 1.576 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.79it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.01it/s]
all 324 324 0.28 0.336 0.207 0.0798
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/120 2.54G 2.093 2.662 1.587 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.91it/s]
all 324 324 0.283 0.285 0.19 0.08
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/120 2.54G 2.079 2.582 1.599 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.86it/s]
all 324 324 0.248 0.295 0.195 0.0752
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/120 2.54G 2.054 2.614 1.556 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.04it/s]
all 324 324 0.279 0.257 0.195 0.0781
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/120 2.54G 2.029 2.631 1.548 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.04it/s]
all 324 324 0.285 0.287 0.2 0.0906
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/120 2.54G 2.056 2.567 1.583 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.70it/s]
all 324 324 0.309 0.331 0.236 0.0906
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/120 2.54G 2.06 2.523 1.6 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.09it/s]
all 324 324 0.302 0.315 0.217 0.0799
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/120 2.54G 2.031 2.472 1.57 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.09it/s]
all 324 324 0.323 0.31 0.238 0.0992
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/120 2.54G 2.07 2.499 1.562 6 640: 100%|██████████| 71/71 [00:15<00:00, 4.63it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.16it/s]
all 324 324 0.34 0.282 0.232 0.0935
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/120 2.54G 2.023 2.516 1.592 9 640: 100%|██████████| 71/71 [00:15<00:00, 4.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.73it/s]
all 324 324 0.37 0.379 0.295 0.116
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/120 2.54G 1.991 2.465 1.568 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.15it/s]
all 324 324 0.352 0.295 0.265 0.109
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/120 2.54G 2.006 2.408 1.538 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.06it/s]
all 324 324 0.379 0.345 0.276 0.114
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/120 2.54G 2.005 2.466 1.578 5 640: 100%|██████████| 71/71 [00:15<00:00, 4.71it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.08it/s]
all 324 324 0.376 0.333 0.265 0.1
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/120 2.54G 2.003 2.343 1.553 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.09it/s]
all 324 324 0.376 0.352 0.272 0.1
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/120 2.54G 1.981 2.413 1.536 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.09it/s]
all 324 324 0.311 0.301 0.229 0.088
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/120 2.54G 1.976 2.459 1.564 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.07it/s]
all 324 324 0.379 0.363 0.276 0.111
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/120 2.54G 1.95 2.372 1.534 9 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.11it/s]
all 324 324 0.371 0.335 0.282 0.109
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/120 2.54G 1.938 2.326 1.517 10 640: 100%|██████████| 71/71 [00:15<00:00, 4.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.17it/s]
all 324 324 0.374 0.333 0.286 0.114
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/120 2.54G 1.966 2.339 1.525 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.09it/s]
all 324 324 0.336 0.36 0.27 0.108
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/120 2.54G 1.942 2.35 1.544 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.15it/s]
all 324 324 0.377 0.308 0.256 0.0949
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/120 2.54G 1.939 2.326 1.541 9 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.01it/s]
all 324 324 0.322 0.42 0.276 0.11
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/120 2.54G 1.927 2.319 1.526 10 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.01it/s]
all 324 324 0.347 0.405 0.289 0.111
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/120 2.54G 1.944 2.271 1.55 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.79it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.14it/s]
all 324 324 0.395 0.341 0.293 0.121
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/120 2.54G 1.946 2.275 1.5 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.09it/s]
all 324 324 0.393 0.36 0.297 0.122
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/120 2.54G 1.91 2.217 1.506 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.79it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.10it/s]
all 324 324 0.39 0.394 0.296 0.116
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/120 2.54G 1.937 2.269 1.502 6 640: 100%|██████████| 71/71 [00:15<00:00, 4.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.61it/s]
all 324 324 0.438 0.376 0.307 0.12
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/120 2.54G 1.905 2.2 1.516 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.12it/s]
all 324 324 0.355 0.368 0.288 0.116
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/120 2.54G 1.951 2.213 1.513 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.10it/s]
all 324 324 0.382 0.414 0.288 0.11
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/120 2.54G 1.884 2.217 1.473 8 640: 100%|██████████| 71/71 [00:15<00:00, 4.62it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.95it/s]
all 324 324 0.387 0.336 0.279 0.116
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/120 2.54G 1.961 2.273 1.53 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.93it/s]
all 324 324 0.341 0.34 0.26 0.109
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
39/120 2.54G 1.874 2.222 1.466 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.70it/s]
all 324 324 0.371 0.413 0.326 0.125
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
40/120 2.54G 1.93 2.229 1.51 6 640: 100%|██████████| 71/71 [00:15<00:00, 4.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.99it/s]
all 324 324 0.43 0.363 0.333 0.131
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
41/120 2.54G 1.879 2.155 1.478 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.10it/s]
all 324 324 0.386 0.332 0.294 0.117
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
42/120 2.54G 1.916 2.219 1.517 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.14it/s]
all 324 324 0.341 0.419 0.298 0.12
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
43/120 2.54G 1.84 2.183 1.451 9 640: 100%|██████████| 71/71 [00:14<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.08it/s]
all 324 324 0.396 0.346 0.302 0.124
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
44/120 2.54G 1.871 2.173 1.475 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.14it/s]
all 324 324 0.349 0.402 0.288 0.108
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
45/120 2.54G 1.881 2.148 1.51 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.13it/s]
all 324 324 0.456 0.421 0.354 0.148
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
46/120 2.54G 1.836 2.113 1.476 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.08it/s]
all 324 324 0.37 0.321 0.263 0.103
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
47/120 2.54G 1.863 2.047 1.469 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.90it/s]
all 324 324 0.367 0.372 0.301 0.116
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
48/120 2.54G 1.867 2.05 1.504 7 640: 100%|██████████| 71/71 [00:15<00:00, 4.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.13it/s]
all 324 324 0.409 0.38 0.311 0.129
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
49/120 2.54G 1.822 2.1 1.463 3 640: 100%|██████████| 71/71 [00:15<00:00, 4.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.74it/s]
all 324 324 0.41 0.379 0.312 0.129
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
50/120 2.54G 1.837 2.099 1.476 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.14it/s]
all 324 324 0.321 0.389 0.262 0.102
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
51/120 2.54G 1.819 2.108 1.45 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.06it/s]
all 324 324 0.367 0.401 0.293 0.118
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
52/120 2.54G 1.84 2.025 1.455 7 640: 100%|██████████| 71/71 [00:15<00:00, 4.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.92it/s]
all 324 324 0.429 0.41 0.334 0.135
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
53/120 2.54G 1.83 2.042 1.469 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.15it/s]
all 324 324 0.394 0.41 0.299 0.123
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
54/120 2.54G 1.814 2.047 1.455 6 640: 100%|██████████| 71/71 [00:15<00:00, 4.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.93it/s]
all 324 324 0.416 0.398 0.333 0.132
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
55/120 2.54G 1.812 1.985 1.434 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.12it/s]
all 324 324 0.403 0.419 0.32 0.124
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
56/120 2.54G 1.783 1.963 1.446 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.98it/s]
all 324 324 0.436 0.421 0.347 0.141
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
57/120 2.54G 1.808 2.034 1.428 4 640: 100%|██████████| 71/71 [00:15<00:00, 4.70it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.20it/s]
all 324 324 0.421 0.396 0.307 0.122
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
58/120 2.54G 1.793 1.954 1.448 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.98it/s]
all 324 324 0.451 0.349 0.301 0.123
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
59/120 2.54G 1.763 1.953 1.44 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.94it/s]
all 324 324 0.403 0.412 0.349 0.138
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
60/120 2.54G 1.798 1.906 1.437 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.18it/s]
all 324 324 0.431 0.359 0.319 0.13
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
61/120 2.54G 1.807 1.97 1.468 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.08it/s]
all 324 324 0.437 0.413 0.34 0.139
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
62/120 2.54G 1.803 1.936 1.445 9 640: 100%|██████████| 71/71 [00:14<00:00, 4.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.21it/s]
all 324 324 0.396 0.445 0.35 0.141
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
63/120 2.54G 1.79 1.923 1.451 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.71it/s]
all 324 324 0.403 0.386 0.306 0.125
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
64/120 2.54G 1.747 1.877 1.404 3 640: 100%|██████████| 71/71 [00:15<00:00, 4.70it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.06it/s]
all 324 324 0.428 0.359 0.313 0.125
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
65/120 2.54G 1.758 1.893 1.435 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.13it/s]
all 324 324 0.39 0.403 0.316 0.124
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
66/120 2.54G 1.754 1.92 1.417 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.17it/s]
all 324 324 0.399 0.375 0.309 0.122
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
67/120 2.54G 1.742 1.87 1.411 10 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.02it/s]
all 324 324 0.407 0.367 0.308 0.128
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
68/120 2.54G 1.718 1.859 1.399 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.98it/s]
all 324 324 0.426 0.343 0.28 0.109
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
69/120 2.54G 1.734 1.871 1.388 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.16it/s]
all 324 324 0.398 0.396 0.316 0.126
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
70/120 2.54G 1.725 1.861 1.404 6 640: 100%|██████████| 71/71 [00:15<00:00, 4.66it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.12it/s]
all 324 324 0.425 0.361 0.328 0.135
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
71/120 2.54G 1.714 1.909 1.403 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.00it/s]
all 324 324 0.399 0.375 0.288 0.119
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
72/120 2.54G 1.704 1.785 1.39 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.03it/s]
all 324 324 0.37 0.395 0.292 0.113
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
73/120 2.54G 1.684 1.808 1.369 7 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.14it/s]
all 324 324 0.396 0.38 0.293 0.117
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
74/120 2.54G 1.683 1.754 1.383 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.08it/s]
all 324 324 0.388 0.43 0.312 0.125
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
75/120 2.54G 1.674 1.804 1.362 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.99it/s]
all 324 324 0.335 0.431 0.316 0.127
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
76/120 2.54G 1.673 1.745 1.378 5 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.07it/s]
all 324 324 0.393 0.363 0.287 0.116
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
77/120 2.54G 1.67 1.796 1.354 5 640: 100%|██████████| 71/71 [00:15<00:00, 4.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.21it/s]
all 324 324 0.42 0.348 0.313 0.133
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
78/120 2.54G 1.657 1.747 1.361 9 640: 100%|██████████| 71/71 [00:14<00:00, 4.79it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.14it/s]
all 324 324 0.457 0.338 0.302 0.118
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
79/120 2.54G 1.666 1.722 1.376 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.79it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.09it/s]
all 324 324 0.406 0.407 0.32 0.125
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
80/120 2.54G 1.656 1.725 1.353 8 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 3.87it/s]
all 324 324 0.468 0.337 0.32 0.128
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
81/120 2.54G 1.663 1.709 1.364 4 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.15it/s]
all 324 324 0.388 0.4 0.317 0.12
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
82/120 2.54G 1.648 1.672 1.351 2 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.04it/s]
all 324 324 0.393 0.4 0.31 0.12
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
83/120 2.54G 1.621 1.696 1.344 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.19it/s]
all 324 324 0.368 0.384 0.301 0.117
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
84/120 2.54G 1.645 1.664 1.333 9 640: 100%|██████████| 71/71 [00:14<00:00, 4.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.01it/s]
all 324 324 0.411 0.41 0.326 0.124
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
85/120 2.54G 1.616 1.645 1.337 9 640: 100%|██████████| 71/71 [00:14<00:00, 4.80it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.14it/s]
all 324 324 0.409 0.42 0.323 0.123
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
86/120 2.54G 1.625 1.671 1.353 3 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.13it/s]
all 324 324 0.448 0.359 0.313 0.118
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
87/120 2.54G 1.606 1.628 1.342 6 640: 100%|██████████| 71/71 [00:14<00:00, 4.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.20it/s]
all 324 324 0.389 0.386 0.309 0.124
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
88/120 2.54G 1.595 1.612 1.326 7 640: 100%|██████████| 71/71 [00:15<00:00, 4.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.15it/s]
all 324 324 0.401 0.394 0.313 0.122
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
89/120 2.54G 1.611 1.58 1.332 9 640: 100%|██████████| 71/71 [00:14<00:00, 4.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.06it/s]
all 324 324 0.373 0.402 0.307 0.119
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
90/120 2.54G 1.595 1.553 1.347 5 640: 100%|██████████| 71/71 [00:15<00:00, 4.65it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:02<00:00, 4.22it/s]
all 324 324 0.395 0.371 0.291 0.111
EarlyStopping: Training stopped early as no improvement observed in last 45 epochs. Best results observed at epoch 45, best model saved as best.pt.
To update EarlyStopping(patience=45) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.
90 epochs completed in 0.458 hours. Optimizer stripped from runs/detect/train51/weights/last.pt, 5.3MB Optimizer stripped from runs/detect/train51/weights/best.pt, 5.3MB Validating runs/detect/train51/weights/best.pt... Ultralytics 8.3.120 🚀 Python-3.10.12 torch-2.7.0+cu126 CUDA:0 (NVIDIA GeForce GTX 1080 Ti, 11165MiB) YOLOv5n summary (fused): 84 layers, 2,503,334 parameters, 0 gradients, 7.1 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 11/11 [00:03<00:00, 3.26it/s]
all 324 324 0.458 0.421 0.353 0.148
BENIGN 174 174 0.424 0.443 0.363 0.158
MALIGNANT 150 150 0.492 0.4 0.344 0.137
Speed: 0.2ms preprocess, 2.6ms inference, 0.0ms loss, 2.0ms postprocess per image
Results saved to runs/detect/train51
ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([0, 1])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7f7f8d136080>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
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[ 0.018453, 0.018453, 0.028509, ..., 1, 1, 1]]), 'Confidence', 'Precision'], [array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,
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[ 0.84, 0.84, 0.79333, ..., 0, 0, 0]]), 'Confidence', 'Recall']]
fitness: np.float64(0.16818767233950643)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.15841, 0.13679])
names: {0: 'BENIGN', 1: 'MALIGNANT'}
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.457845233635291), 'metrics/recall(B)': np.float64(0.421264367816092), 'metrics/mAP50(B)': np.float64(0.3534774544660081), 'metrics/mAP50-95(B)': np.float64(0.14759991876989514), 'fitness': np.float64(0.16818767233950643)}
save_dir: PosixPath('runs/detect/train51')
speed: {'preprocess': 0.20119547556487866, 'inference': 2.585076048602293, 'loss': 0.0007237727397385939, 'postprocess': 2.0048052164507504}
task: 'detect'
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# Ruta a tu carpeta de resultados
ruta = 'runs/detect/train40/'
# Lista de imágenes
imagenes = ['val_batch1_pred.jpg', 'val_batch1_labels.jpg', 'P_curve.png', 'val_batch2_pred.jpg',
'labels.jpg', 'R_curve.png', 'PR_curve.png','val_batch0_pred.jpg', 'train_batch2.jpg', 'results.png',
'confusion_matrix_normalized.png', 'labels_correlogram.jpg','F1_curve.png', 'val_batch2_labels.jpg', 'train_batch1.jpg','confusion_matrix.png', 'val_batch0_labels.jpg', 'train_batch0.jpg']
for img in imagenes:
try:
img_path = ruta + img
image = mpimg.imread(img_path)
plt.figure(figsize=(10, 6))
plt.imshow(image)
plt.axis('off')
plt.title(img)
plt.show()
except FileNotFoundError:
print(f"{img} no encontrado.")
import pandas as pd
# Cargamos los resultados
df = pd.read_csv("runs/detect/train40/results.csv")
# Encontramos la fila con el mejor mAP_0.5
best_row = df[df['metrics/mAP50(B)'] == df['metrics/mAP50(B)'].max()]
# Vemos las métricas
print(best_row[['epoch', 'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)']])
epoch metrics/precision(B) metrics/recall(B) metrics/mAP50(B) 27 28 0.39796 0.40287 0.32845
ruta = 'runs/detect/train41/'
for img in imagenes:
try:
img_path = ruta + img
image = mpimg.imread(img_path)
plt.figure(figsize=(10, 6))
plt.imshow(image)
plt.axis('off')
plt.title(img)
plt.show()
except FileNotFoundError:
print(f"{img} no encontrado.")
import pandas as pd
# Cargamos los resultados
df = pd.read_csv("runs/detect/train41/results.csv")
# Encontramos la fila con el mejor mAP_0.5
best_row = df[df['metrics/mAP50(B)'] == df['metrics/mAP50(B)'].max()]
# Vemos las métricas
print(best_row[['epoch', 'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)']])
epoch metrics/precision(B) metrics/recall(B) metrics/mAP50(B) 44 45 0.401 0.38379 0.34571
ruta = 'runs/detect/train43/'
for img in imagenes:
try:
img_path = ruta + img
image = mpimg.imread(img_path)
plt.figure(figsize=(10, 6))
plt.imshow(image)
plt.axis('off')
plt.title(img)
plt.show()
except FileNotFoundError:
print(f"{img} no encontrado.")
import pandas as pd
# Cargamos los resultados
df = pd.read_csv("runs/detect/train43/results.csv")
# Encontramos la fila con el mejor mAP_0.5
best_row = df[df['metrics/mAP50(B)'] == df['metrics/mAP50(B)'].max()]
# Vemos las métricas
print(best_row[['epoch', 'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)']])
epoch metrics/precision(B) metrics/recall(B) metrics/mAP50(B) 49 50 0.47706 0.38816 0.35294
import matplotlib.image as mpimg
ruta = 'runs/detect/train45/'
imagenes = ['val_batch1_pred.jpg', 'val_batch1_labels.jpg', 'P_curve.png', 'val_batch2_pred.jpg',
'labels.jpg', 'R_curve.png', 'PR_curve.png','val_batch0_pred.jpg', 'train_batch2.jpg', 'results.png',
'confusion_matrix_normalized.png', 'labels_correlogram.jpg','F1_curve.png', 'val_batch2_labels.jpg', 'train_batch1.jpg','confusion_matrix.png', 'val_batch0_labels.jpg', 'train_batch0.jpg']
for img in imagenes:
try:
img_path = ruta + img
image = mpimg.imread(img_path)
plt.figure(figsize=(10, 6))
plt.imshow(image)
plt.axis('off')
plt.title(img)
plt.show()
except FileNotFoundError:
print(f"{img} no encontrado.")
import pandas as pd
# Cargamos los resultados
df = pd.read_csv("runs/detect/train45/results.csv")
# Encontramos la fila con el mejor mAP_0.5
best_row = df[df['metrics/mAP50(B)'] == df['metrics/mAP50(B)'].max()]
# Vemos las métricas
print(best_row[['epoch', 'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)']])
epoch metrics/precision(B) metrics/recall(B) metrics/mAP50(B) 39 40 0.51028 0.3946 0.35993
ruta = 'runs/detect/train53/'
for img in imagenes:
try:
img_path = ruta + img
image = mpimg.imread(img_path)
plt.figure(figsize=(10, 6))
plt.imshow(image)
plt.axis('off')
plt.title(img)
plt.show()
except FileNotFoundError:
print(f"{img} no encontrado.")
import pandas as pd
# Cargamos los resultados
df = pd.read_csv("runs/detect/train53/results.csv")
# Encontramos la fila con el mejor mAP_0.5
best_row = df[df['metrics/mAP50(B)'] == df['metrics/mAP50(B)'].max()]
# Vemos las métricas
print(best_row[['epoch', 'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)']])
epoch metrics/precision(B) metrics/recall(B) metrics/mAP50(B) 69 70 0.41051 0.4369 0.35693
ruta = 'runs/detect/train51/'
for img in imagenes:
try:
img_path = ruta + img
image = mpimg.imread(img_path)
plt.figure(figsize=(10, 6))
plt.imshow(image)
plt.axis('off')
plt.title(img)
plt.show()
except FileNotFoundError:
print(f"{img} no encontrado.")
import pandas as pd
# Cargamos los resultados
df = pd.read_csv("runs/detect/train51/results.csv")
# Encontramos la fila con el mejor mAP_0.5
best_row = df[df['metrics/mAP50(B)'] == df['metrics/mAP50(B)'].max()]
# Vemos las métricas
print(best_row[['epoch', 'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)']])
epoch metrics/precision(B) metrics/recall(B) metrics/mAP50(B) 44 45 0.45629 0.42126 0.3537